ArticleBMC medical education2025
Artificial intelligence use as a key predictor of clinical performance in nursing students: a cross-sectional study from Iran.
Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
objectivesThis study aimed to investigate the relationship between AI use and clinical performance among undergraduate nursing students, while controlling for key demographic variables.
methodsA 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.
resultsAI 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).
conclusionAI 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.