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Nursing Reports
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Integrating Artificial Intelligence and Big Data into Nursing Practice, Education...
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Integrating Artificial Intelligence and Big Data into Nursing Practice, Education and Policy

A special issue of Nursing Reports (ISSN 2039-4403). This special issue belongs to the section "Artificial Intelligence and Digital Innovations in Nursing Care".

Deadline for manuscript submissions: 30 April 2027 | Viewed by 346

Editor

Prof. Dr. Connie White Delaney

E-Mail Website
Guest Editor
School of Nursing, University of Minnesota, Minneapolis, MN 55455, USA
Interests: informatics; artificial intelligence; leadership; health innovation

Special Issue Information

Dear Colleagues,

Integrating artificial intelligence (AI) and big data is essential for advancing nursing, ensuring that we can empower our students, professionals, and profession to lead in a rapidly evolving whole-person health care landscape. This Special Issue acts as a call to action to embed these technologies across nursing education, practice, research, and policy, with humanity always at the forefront. In education, we must prepare nurses to critically engage with AI and data, fostering informatics competencies that enhance clinical decision-making, reduce documentation burden, and support personalized, evidence-based care. In practice, AI and big data offer powerful tools to optimize workflows, leverage standardized data and small and large language models to make nursing contributions visible, and improve patient engagement and outcomes. In research, these tools enable us to generate robust insights, quantify the value of nursing care, and address health disparities. In policy, nurses must champion ethical AI governance, advocate for interoperable data standards, and ensure that nursing's voice is central in shaping digital health strategies. By leading with care, love and discernment, we can harness AI and big data to advance health for all through the unwavering foundations of human wisdom and relationships and social justice.

Prof. Dr. Connie White Delaney
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Nursing Reports is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • big data
  • nursing
  • education research
  • practice
  • policy

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Published Papers (1 paper)

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Research

12 pages, 501 KB  
Article
The Associations of AI Literacy, AI Self-Efficacy and AI Attitudes Among Nursing Students: A Cross-Sectional Path Analysis
by Shinhi Han, Hee Sun Kang, Philip Gimber and Sunghyun Lim
Nurs. Rep. 2026, 16(8), 292; https://doi.org/10.3390/nursrep16080292 - 21 Aug 2026
Viewed by 118
Abstract
Background: Generative Artificial Intelligence (GenAI) is increasingly integrated into nursing education, yet structured AI literacy training and ethical guidance remain limited. Consequently, nursing students often rely on informal learning, resulting in variability in AI readiness, confidence, and responsible use. Aims: This [...] Read more.
Background: Generative Artificial Intelligence (GenAI) is increasingly integrated into nursing education, yet structured AI literacy training and ethical guidance remain limited. Consequently, nursing students often rely on informal learning, resulting in variability in AI readiness, confidence, and responsible use. Aims: This study was conducted to examine (1) whether AI literacy was positively associated with AI self-efficacy and AI attitudes and (2) whether AI self-efficacy mediated the relationship between AI literacy and AI attitudes. Methods: A cross-sectional survey using convenience sampling was conducted with 100 prelicensure nursing students in New York City. Data were collected using the AI Literacy Scale (AILS), AI Self-Efficacy Scale (AISES), and Generative AI Attitude Scale (GAIAS). Correlation and path analyses were performed using SPSS and Amos 30.0. Results: The participants had a mean age of 30.25 years, and 71% were women. AI literacy and AI self-efficacy were both positively associated with AI attitudes (all p < 0.001). Path analysis showed that AI literacy significantly predicted AI self-efficacy (β = 0.39, p < 0.001) and AI attitudes (β = 0.28, p = 0.003). AI self-efficacy significantly predicted AI attitudes (β = 0.31, p = 0.001) and partially mediated the relationship between AI literacy and AI attitudes. Conclusions: AI self-efficacy partially mediated the relationship between AI literacy and AI attitudes. Nursing curricula may benefit from structured AI education that integrates guided GenAI practice, case-based learning, and faculty feedback. Such educational frameworks warrant further empirical investigation regarding their potential to foster AI literacy, AI self-efficacy, and positive attitudes toward responsible AI integration, particularly through longitudinal studies assessing subsequent behavioral outcomes. Full article
(This article belongs to the Special Issue Integrating Artificial Intelligence and Big Data into Nursing Practice, Education and Policy)
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