Background <p>There is a potential shortage of information-based teaching resources in Northeast China, and the artificial intelligence literacy of nursing undergraduates and related factors are not clear.</p> Methods <p>This is a cross-sectional survey study conducted in the northeastern region of China. In this study, 546 nursing undergraduates were studied. The data was collected through a mobile app-based survey in October 2024. We assessed demographic factors and artificial intelligence literacy.</p> Results <p>The total score of artificial intelligence literacy of nursing undergraduates was 134.40 ± 4.38. Regression analysis showed that gender, monthly household income, attitude towards artificial intelligence, and whether or not participating in artificial intelligence-related education/training had a significant negative effect on artificial intelligence literacy (<i>p</i> &lt; .05).</p> Conclusions <p>It is recommended to formulate effective measures in combination with relevant factors to improve the artificial intelligence literacy of nursing undergraduates in areas with a potential shortage of information-based teaching resources and promote the digital transformation of nursing undergraduates’ future careers.</p> Clinic trial number <p>Not applicable.</p>

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Artificial intelligence literacy and related factors among nursing undergraduates in Northeast China

  • Dong Chen,
  • Xinjing Zhang,
  • Shasha Zhang,
  • Yanyan Pan,
  • Chongkuan Zhu,
  • Lei Wang

摘要

Background

There is a potential shortage of information-based teaching resources in Northeast China, and the artificial intelligence literacy of nursing undergraduates and related factors are not clear.

Methods

This is a cross-sectional survey study conducted in the northeastern region of China. In this study, 546 nursing undergraduates were studied. The data was collected through a mobile app-based survey in October 2024. We assessed demographic factors and artificial intelligence literacy.

Results

The total score of artificial intelligence literacy of nursing undergraduates was 134.40 ± 4.38. Regression analysis showed that gender, monthly household income, attitude towards artificial intelligence, and whether or not participating in artificial intelligence-related education/training had a significant negative effect on artificial intelligence literacy (p < .05).

Conclusions

It is recommended to formulate effective measures in combination with relevant factors to improve the artificial intelligence literacy of nursing undergraduates in areas with a potential shortage of information-based teaching resources and promote the digital transformation of nursing undergraduates’ future careers.

Clinic trial number

Not applicable.