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Cited by 146Artificial Intelligence in Healthcare and EducationArtificial Intelligence in EducationHealthcare Education and Workforce Issues

The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.

Factors influencing student nurses' readiness to adopt artificial intelligence (AI) in their studies and their perceived barriers to accessing AI technology: A cross-sectional study

Labrague, Leodoro J., Aguilar-Rosales, Rheajane, Yboa, Begonia C., Sabio, Jeanette B.·2023·Nurse Education Today
DOI 10.1016/j.nedt.2023.105945
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Abstract

Background With the projected significant increase in the use of AI in nursing education, it becomes vital for nurse faculty to adequately equip student nurses with the necessary competences to effectively utilize AI in their studies. Ensuring that student nurses are prepared and ready to embrace AI technology is imperative for their successful integration into the healthcare workforce.Objective This study aimed to examine student nurses' readiness to embrace AI technology, explore associated factors, and identify perceived barriers to accessing AI technology.Design Cross sectional study.Settings One public-owned nursing school in the Philippines.Participants Three hundred twenty-three student nurses.Methods Data were collected using structured questionnaires. Descriptive statistics and multivariable analysis were performed to analyze the data.Results The results revealed that student nurses demonstrated moderate readiness to embrace AI in their studies (M = 2.906, SD = 0.692) and perceived moderate barriers to accessing AI technology (M = 2.336, SD = 0.719). Factors associated with students' readiness to embrace AI included self-rated technological proficiency (β = 0.170, p = 0.014), understanding of AI-powered technologies (β = 0.260, p < 0.001), and perceived AI use in nursing practice (β = 0.153, p = 0.022). The study also identified potential barriers to accessing AI technology, such as lack of computer skills to navigate AI, lack of AI knowledge and awareness, and time constraints.Conclusion The findings of this study provided valuable insights into the factors influencing student nurses' attitudes towards AI and shed light on their perceived barriers to accessing AI technology. By enhancing technological proficiency, increasing AI understanding, and providing practical experiences, nurse faculty can better prepare future nurses to effectively navigate the AI-driven healthcare environment and contribute to improved patient care outcomes.

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Crossref · 10.1016/j.nedt.2023.105945

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Citations

Cites

Research works printed but not linked: 2

  1. Buchanan, 2021, Predicted influences of artificial intelligence on the domains of nursing: Scoping review, JMIR Nurs., 4
  2. Damerji, 2021, Mediating effect of use perceptions on technology readiness and adoption of artificial intelligence in accounting, Acc. Educ., 30, 107, 10.1080/09639284.2021.1872035 · DOI

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Metadata

Authors
Labrague, L. J., Aguilar-Rosales, R., Yboa, B. C., & Sabio, J. B.
Affiliations
  • Loyola University Chicago (US)
  • Samar State University (PH)
Corresponding author
Labrague, Leodoro J.
ORCID
Labrague, Leodoro J.
Venue
Nurse Education Today
Publisher
Elsevier BV
Published
2023-11
Volume
130
Pages
105945
ISSN
0260-6917 (ISSN-L)
DOI
10.1016/j.nedt.2023.105945
OpenAlex
W4386002719
Cited by
145
References
31
Citations by year
2026: 55 · 2025: 60 · 2024: 27 · 2023: 2
FWCI
5.83
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Nurse Education Today (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Education › Artificial Intelligence in Education
  • Health Sciences › Nursing › Leadership and Management › Healthcare Education and Workforce Issues
Keywords
artificial intelligence, nursing students, technology readiness, perceived barriers, nursing education, technological competence, computer skills, time constraints
MeSH terms
Artificial Intelligence, Cross-Sectional Studies, Faculty, Humans, Nurses, Students, Nursing

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