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Generative artificial intelligence literacy for learning and individual creativity: A cross-sectional study among
Remziye Semerci Şahin1, Selma Durmuş Sarıkahya2, Sevil Çınar Özbay3
1Koç University, School of Nursing, Department of Pediatric Nursing, İstanbul, Turkey.
Nurse Education Today
|July 4, 2026
Summary
Generative artificial intelligence (GenAI) literacy is positively linked to creativity in nursing students. Key predictors include needs analysis and critical thinking skills, highlighting GenAI
Area of Science:
- Nursing Education
- Artificial Intelligence in Higher Education
- Cognitive Science
Background:
- Generative artificial intelligence (GenAI) integration is transforming higher education.
- Limited empirical data exists on GenAI literacy's impact on creativity in nursing students.
- Understanding this relationship is crucial for curriculum development.
Purpose of the Study:
- To investigate the correlation between GenAI literacy for learning and individual creativity in nursing students.
- To identify specific GenAI literacy dimensions that predict creativity.
- To inform pedagogical strategies in nursing education.
Main Methods:
- Quantitative, descriptive, correlational cross-sectional study design.
- Involved 508 undergraduate nursing students from two Turkish universities.
- Utilized validated scales for GenAI literacy and individual creativity; analyzed using Pearson correlation and multiple regression.
Main Results:
- Significant positive correlations were found between overall GenAI literacy and individual creativity (r=0.59).
- Needs Analysis (r=0.52) and Critical Thinking (r=0.57) emerged as significant predictors of creativity.
- The regression model accounted for 36% of the variance in creativity.
Conclusions:
- Enhanced GenAI literacy for learning is associated with increased individual creativity among nursing students.
- Cognitively focused GenAI competencies, particularly needs analysis and critical thinking, are vital for fostering creativity.
- Limitations include a cross-sectional design and specific geographic context, warranting further research for causal inference and broader generalizability.
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