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Generative Artificial Intelligence Literacy for Learning and Individual Creativity: a cross-sectional study among nursing students

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SCHOOL OF NURSING
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Durmus, S.
Ozbay, S. C.
Kudubes, A. A.

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Language

eng

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N/A

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Abstract

Generative artificial intelligence (GenAI) tools are increasingly integrated into higher education, reshaping learning, cognitive engagement, and creative problem-solving. However, empirical evidence on the relationship between GenAI literacy and individual creativity in nursing education remains limited. Objective To examine the relationship between GenAI literacy for learning and individual creativity among nursing students and to explore the predictive role of GenAI literacy dimensions on creativity. Design Quantitative, descriptive, correlational cross-sectional study. Setting Two public universities in Türkiye: Artvin Çoruh University and Bilecik Şeyh Edebali University. Participants A total of 508 undergraduate nursing students participated. Methods Data were collected using the Sociodemographic Information Form, the Individual Creativity Scale, and the Generative Artificial Intelligence Literacy for Learning Scale. Pearson correlation and multiple regression analyses were performed. Results Individual creativity showed significant positive correlations with overall GenAI literacy (r = 0.59) and its subdimensions, including Needs Analysis (r = 0.52), Autonomous Learning (r = 0.43), Critical Thinking (r = 0.57), and Prompt and Language Skills (r = 0.46) (all p < .001). The regression model explained 36% of the variance in creativity, with Needs Analysis and Critical Thinking as significant predictors. Conclusion Higher GenAI literacy for learning is associated with greater individual creativity among nursing students, with needs analysis and critical thinking as key predictors, underscoring the value of cognitively oriented GenAI literacy competencies in nursing education. However, findings are based on a cross-sectional design at only two universities in Türkiye, limiting causal inference and generalizability to broader nursing student populations.

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Elsevier BV

Subject

Nursing, Education, Artificial intelligence, Educational technology

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Nurse Education Today

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DOI

10.1016/j.nedt.2026.107248

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