Abstract
BACKGROUND: The integration of artificial intelligence (AI) into healthcare education is rapidly evolving, yet its impact on clinical performance among nursing students remains underexplored, particularly in resource-constrained settings. OBJECTIVES: This study aimed to investigate the relationship between AI use and clinical performance among undergraduate nursing students, while controlling for key demographic variables. METHODS: A cross-sectional study was conducted with 134 undergraduate nursing students from Abadan University of Medical Sciences, Iran, in 2024. Data were collected on AI use (Artificial Intelligence in Nursing Questionnaire), and Clinical Performance Questionnaire (CPQ). Data were analyzed using IBM SPSS Statistics (v26). Descriptive statistics, Pearson correlation, multiple linear regression, and univariate general linear modeling (GLM) were employed. RESULTS: AI use demonstrated a significant positive correlation with overall clinical performance (*r* = 0.424, *p* < 0.001). In the multiple regression model, AI use was the only significant predictor of clinical performance (β = 0.425, *p* < 0.001), explaining 21.1% of the variance (*R²* = 0.211). Demographic variables (gender, academic term, age level) were non-significant. A univariate GLM confirmed a significant main effect for AI use (*F*(1,111) = 19.672, *p* < 0.001), independent of all demographic factors. Simple linear regressions revealed that AI use significantly predicted performance across all clinical subscales, with the strongest effects in Research (*R²* = 0.166), Patient-Centered Care (*R²* = 0.146), and Personal Management (*R²* = 0.127). CONCLUSION: AI use is a robust and independent predictor of clinical performance among nursing students. These findings underscore the transformative potential of AI in clinical education and advocate for the systematic integration of AI literacy into nursing curricula to enhance evidence-based practice, critical thinking, and patient-centered care.