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Background <p>The integration of artificial intelligence (AI) into nursing education and practice has demonstrated significant potential to enhance efficiency, reduce errors, and optimize healthcare delivery. However, the successful implementation of AI requires careful consideration of the knowledge, attitudes, and practices of nursing professionals and students.</p> Aim <p>To examine the association between knowledge, attitudes, and practices (KAP) regarding AI use in nursing education and clinical practice.</p> Method <p>Systematic review and meta-analysis. Following PRISMA guidelines, a comprehensive search was conducted across three major databases (SCOPUS, Web of Science, and PubMed) for studies published between January 2019 and April 2025.</p> Results <p>Ten qualified cross-sectional studies were identified from an initial pool of 283 records. A random-effects meta-analysis revealed a moderate positive correlation between knowledge and attitudes {Pearson’s correlation (<i>r</i>) = 0.43, 95% confidence interval (CI) = [0.31, 0.53]}, a moderate attitude–practice relationship {Pearson’s correlation (<i>r</i>) = 0.46, 95% CI = [0.32, 0.59]}, and a weak knowledge–practice association {Pearson’s correlation (<i>r</i>) = 0.26, 95% CI = [0.13, 0.38]}. Significant heterogeneity was observed in the analysis [between-study variance (<i>τ²</i>) = 0.03–0.05; inconsistency (<i>I²</i>) = 89.88%–95.02%; Cochran’s Q (<i>Q</i>) = 16.44–157.00, <i>p</i> &lt; 0.001), thereby suggesting that contextual factors such as institutional policies and prior AI exposure may influence these relationships.</p> Conclusions <p>These findings partially aligned with the KAP theoretical framework, thus highlighting the significant association between AI knowledge, attitude, and clinical implementation. We recommend integrated AI education that emphasizes attitude formation, targeted training to address implementation barriers, and organizational support systems for AI integration. Future research should employ longitudinal designs to establish causal relationships and examine the contextual factors influencing AI use across diverse healthcare settings.</p>

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Knowledge, attitudes, and practices related to the use of artificial intelligence in nursing education and practice: a systematic review and meta-analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月2日

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  • Abdulaziz Mofdy Almarwani,
  • Mona Ibrahim Hebeshy

摘要

Background

The integration of artificial intelligence (AI) into nursing education and practice has demonstrated significant potential to enhance efficiency, reduce errors, and optimize healthcare delivery. However, the successful implementation of AI requires careful consideration of the knowledge, attitudes, and practices of nursing professionals and students.

Aim

To examine the association between knowledge, attitudes, and practices (KAP) regarding AI use in nursing education and clinical practice.

Method

Systematic review and meta-analysis. Following PRISMA guidelines, a comprehensive search was conducted across three major databases (SCOPUS, Web of Science, and PubMed) for studies published between January 2019 and April 2025.

Results

Ten qualified cross-sectional studies were identified from an initial pool of 283 records. A random-effects meta-analysis revealed a moderate positive correlation between knowledge and attitudes {Pearson’s correlation (r) = 0.43, 95% confidence interval (CI) = [0.31, 0.53]}, a moderate attitude–practice relationship {Pearson’s correlation (r) = 0.46, 95% CI = [0.32, 0.59]}, and a weak knowledge–practice association {Pearson’s correlation (r) = 0.26, 95% CI = [0.13, 0.38]}. Significant heterogeneity was observed in the analysis [between-study variance (τ²) = 0.03–0.05; inconsistency (I²) = 89.88%–95.02%; Cochran’s Q (Q) = 16.44–157.00, p < 0.001), thereby suggesting that contextual factors such as institutional policies and prior AI exposure may influence these relationships.

Conclusions

These findings partially aligned with the KAP theoretical framework, thus highlighting the significant association between AI knowledge, attitude, and clinical implementation. We recommend integrated AI education that emphasizes attitude formation, targeted training to address implementation barriers, and organizational support systems for AI integration. Future research should employ longitudinal designs to establish causal relationships and examine the contextual factors influencing AI use across diverse healthcare settings.