ArticleJournal of evaluation in clinical practice2026
Artificial Intelligence Literacy Levels and Attitudes Toward Artificial Intelligence Technology Among Intensive Care Nurses: A Cross-Sectional Study.
Article in Journal of evaluation in clinical practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe artificial intelligence (AI) literacy of intensive care nurses and their behaviors in interacting with AI is crucial for evaluating the impact of AI on patient care and treatment. The aim of this study is to determine the AI literacy levels of intensive care nurses, their attitudes toward AI technologies, and the factors associated with these attitudes.
methodsThe sample for this cross-sectional study consisted of 280 nurses working in the intensive care units of two hospitals. Data were collected face-to-face using a Personal Information Form, an Artificial Intelligence Literacy Scale (AILS), and an Artificial Intelligence Attitude Scale (AIAS-4). Descriptive statistics, Pearson correlation, independent samples t-test, one-way ANOVA, and multiple linear regression analyses were used to analyze the data.
resultsThe mean age of the participants was 33.47 ± 7.12. 63.9% of the nurses were female, 48.9% were married, and 72.1% had a bachelor's degree or higher. The vast majority of nurses (96.4%) had not received any training in AI, and 50% had no experience in this field. The mean AIAS-4 score of intensive care nurses was 6.26 ± 2.74 (min = 1, max = 10) and the mean AILS score was 39.87 ± 6.69 (min = 16, max = 60). The results indicated a strong, positive, and statistically significant correlation between AIAS-4 and AILS (r = 0.72, p < 0.05). Among the variables included in the model, AILS score, age, gender, marital status, and education level were found to be statistically significant predictors (p < 0.05), explaining 64% of the variance (Adj. R
conclusionIn conclusion, the regression model revealed that age was a significant predictor, and individuals aged 36 and over had more negative attitudes toward AI technologies. In contrast, higher levels of AI literacy, male gender, being single, and having a bachelor's degree or higher were found to be significant positive predictors of attitudes toward AI technologies. It is recommended that practice-based training programs emphasizing clinical benefits, aimed at improving AI literacy and attitudes toward AI technologies among intensive care nurses, be expanded.
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