ArticleBMC medical education2026
The effect of artificial intelligence-supported applications on critical thinking motivation and clinical decision making in nursing students: path analysis.
Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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1 citing paper in PubMed.
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4 authors.
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Abstract
backgroundArtificial intelligence (AI)-supported applications are increasingly integrated into nursing education and have the potential to influence students' cognitive and decision-making processes. Understanding how nursing students' attitudes toward AI relate to critical thinking motivation and clinical decision-making is essential for designing pedagogically effective AI-enhanced learning environments. PURPOSE: This study aimed to examine the relationship between nursing students' attitudes toward artificial intelligence, critical thinking motivation, and clinical decision-making attitudes using path analysis. MATERIALS AND
methodsThis cross-sectional quantitative study was conducted between 9 and 20 October 2025 with 419 nursing students who had clinical experience at a state university in the southeastern region of Türkiye. In the study, it was census of eligible students' method and single center. Data were collected online using the Student Demographic Information Form, the Artificial Intelligence Attitude Scale for Nurses, the Critical Thinking Motivation Scale, and the Clinical Decision Making in Nursing Scale. Descriptive statistics, correlation analysis, multiple regression, and path analysis were performed using SPSS 26.0 and R 4.5.1.
resultsStudents reported moderately positive attitudes toward artificial intelligence (mean = 3.26 ± 0.38), high levels of critical thinking motivation (mean = 4.31 ± 0.93), and moderately high clinical decision-making attitudes (mean = 3.26 ± 0.33). Attitudes toward AI were positively associated with critical thinking motivation; however, no significant direct association with clinical decision-making attitudes was observed. Critical thinking motivation was a strong predictor of clinical decision-making attitudes and fully mediated the relationship between AI attitudes and clinical decision making. The path model explained 24% (β = 0.488) of the variance in critical thinking motivation and 49% (β = 0.700) of the variance in clinical decision-making attitudes. The standardized indirect effect of AI attitudes on clinical decision-making through critical thinking motivation was significant (β = 0.341, 95% CI [0.232, 0.355]).
conclusionPositive attitudes toward artificial intelligence were associated with stronger critical thinking motivation among nursing students, which in turn was associated with higher clinical decision-making attitudes. Critical thinking motivation can mediate nursing students' delivery of quality care services. AI-supported educational applications may be most effective when designed to explicitly promote critical thinking within structured and pedagogically sound learning environments. CLINICAL TRIAL NUMBER: Not applicable.
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