Abstract
Background: The rapid integration of artificial intelligence (AI) and data-driven decision-making in healthcare requires nurses to develop strong AI/data literacy.[1,2] Literature and industry reviews identified inconsistent data literacy definitions and few nursing-focused tools,[3] underscoring the need for validated instruments for nursing.
Purpose: To (1) review existing AI/data literacy instruments, (2) conduct item-level mapping to four frameworks representing data processes and competencies, and (3) guide nursing-specific instrument development.
Methods: Eleven international experts in nursing informatics and data science reviewed AI literacy scales from Lintner’s review[4] and five nurse-focused data literacy scales identified via Google Scholar. From 24 instruments, 475 items were extracted into Excel and mapped to: (1) Harvard Data Life Cycle,[5] (2) American Association of Colleges of Nursing Essentials Domain 8 Informatics and Healthcare Technologies (Entry vs. Advanced),[6] (3) Quality and Safety Education for Nurses Informatics Knowledge–Skill–Attitude (KSA),[7] and (4) Technical–Cognitive–Communicative–Ethical (TCCE) schema developed by our team. Each item was coded independently by at least four reviewers; discrepancies were resolved by consensus. Cross-tabulations summarized item distributions, and items were reviewed to identify the literacies represented, informing coverage and gaps.
Results: Mapping to the Data Life Cycle showed highest concentration in Interpretation (29.7%), followed by Analysis (16.8%), Processing (13.5%), Generation (12.4%), Management (11.8%), Collection (7.6%), Visualization (4.8%), and Storage (1.3%); 10 items applied across all stages. Items were Entry (48.8%) and Advanced (51.2%) levels. Knowledge items dominated (60.3%) over Attitudes (20.6%) and Skills (19.1%). Under TCCE, items were primarily Cognitive (47.1%) and Technical (32.5%), with fewer Ethical (16.8%) and Communicative (3.7%). Results provide a structured foundation for nursing-specific AI/data literacy instrument development, identifying missing domains and guiding future item creation.
Conclusion: Findings revealed a predominance of knowledge-oriented cognitive content with limited skill-based, ethical, and communicative coverage. Nursing-tailored AI/data literacy instruments are urgently needed, incorporating clinical scenarios, ethics, and evolving health systems, and must be rigorously tested with nurses to ensure relevance and practicality.
Notes
References:
1. Wei, Q., Pan, S., Liu, X., Hong, M., Nong, C., & Zhang, W. (2025). The integration of AI in nursing: Addressing current applications, challenges, and future directions. Frontiers in medicine, 12, 1545420. https://doi.org/10.3389/fmed.2025.1545420
2. Bergren, M. D., & Maughan, E. D. (2020). Data and information literacy: A fundamental nursing competency. NASN School Nurse, 35(3), 140–142. https://doi.org/10.1177/1942602X20913249
3. Lee, M. A., Vyas, P., D'Agostino, F., Wieben, A., Coviak, C., Mullen-Fortino, M., Park, S., Sileo, M., Nogueira de Souza, E., Brown, S., Role, J., Reger, A., & Pruinelli, L. (2025). Empowering nurses through data literacy and data science literacy: Insights from a state-of-the-art literature review. Advances in Nursing Science, 48(3), 211–227. https://doi.org/10.1097/ANS.0000000000000546
4. Lintner T. (2024). A systematic review of AI literacy scales. NPJ Science of Learning, 9(1), 50. https://doi.org/10.1038/s41539-024-00264-4
5. Stobierski, T. (2021, February 2). 8 Steps in the Data Life Cycle. Harvard Business School. https://online.hbs.edu/blog/post/data-life-cycle
6. American Association of Colleges of Nursing. (2021). The Essentials: Core competencies for professional nursing education. Accessible online at https://www.aacnnursing.org/Portals/0/PDFs/Publications/Essentials-2021.pdf
7. Quality and Safety Education for Nurses Institute. (2022). QSEN competencies. Quality and Safety Education for Nurses. https://www.qsen.org/competencies-pre-licensure-ksas
Sigma Membership
Beta Beta (Dallas)
Type
Presentation
Format Type
Text-based Document
Study Design/Type
Other
Research Approach
Other
Keywords:
Competence, Workforce, Instrument and Tool Development, Artificial Intelligence, Scientific Literacy
Recommended Citation
Lee, Mikyoung Angela; D'Agostino, Fabio; Wieben, Ann; Vyas, Pankaj; Alreshidi, Bader G.; Pruinelli, Lisiane; Sileo, Marisa; Role, Jethrone; Bullion, Holly; Nogueira de Souza, Emiliane; and Northcote, Janet, "AI and Data Literacy Scales: Review and Item Analysis for Nursing-Specific Assessment" (2026). International Nursing Research Congress (INRC). 228.
https://www.sigmarepository.org/inrc/2026/presentations_2026/228
Conference Name
37th International Nursing Research Congress
Conference Host
Sigma Theta Tau International
Conference Location
Toronto, Ontario, Canada
Conference Year
2026
Rights Holder
All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record. All permission requests should be directed accordingly and not to the Sigma Repository. All submitting authors or publishers have affirmed that when using material in their work where they do not own copyright, they have obtained permission of the copyright holder prior to submission and the rights holder has been acknowledged as necessary.
Review Type
Abstract Review Only: Reviewed by Event Host
Acquisition
Proxy-submission
Date of Issue
2026-08-26
AI and Data Literacy Scales: Review and Item Analysis for Nursing-Specific Assessment
Toronto, Ontario, Canada
Background: The rapid integration of artificial intelligence (AI) and data-driven decision-making in healthcare requires nurses to develop strong AI/data literacy.[1,2] Literature and industry reviews identified inconsistent data literacy definitions and few nursing-focused tools,[3] underscoring the need for validated instruments for nursing.
Purpose: To (1) review existing AI/data literacy instruments, (2) conduct item-level mapping to four frameworks representing data processes and competencies, and (3) guide nursing-specific instrument development.
Methods: Eleven international experts in nursing informatics and data science reviewed AI literacy scales from Lintner’s review[4] and five nurse-focused data literacy scales identified via Google Scholar. From 24 instruments, 475 items were extracted into Excel and mapped to: (1) Harvard Data Life Cycle,[5] (2) American Association of Colleges of Nursing Essentials Domain 8 Informatics and Healthcare Technologies (Entry vs. Advanced),[6] (3) Quality and Safety Education for Nurses Informatics Knowledge–Skill–Attitude (KSA),[7] and (4) Technical–Cognitive–Communicative–Ethical (TCCE) schema developed by our team. Each item was coded independently by at least four reviewers; discrepancies were resolved by consensus. Cross-tabulations summarized item distributions, and items were reviewed to identify the literacies represented, informing coverage and gaps.
Results: Mapping to the Data Life Cycle showed highest concentration in Interpretation (29.7%), followed by Analysis (16.8%), Processing (13.5%), Generation (12.4%), Management (11.8%), Collection (7.6%), Visualization (4.8%), and Storage (1.3%); 10 items applied across all stages. Items were Entry (48.8%) and Advanced (51.2%) levels. Knowledge items dominated (60.3%) over Attitudes (20.6%) and Skills (19.1%). Under TCCE, items were primarily Cognitive (47.1%) and Technical (32.5%), with fewer Ethical (16.8%) and Communicative (3.7%). Results provide a structured foundation for nursing-specific AI/data literacy instrument development, identifying missing domains and guiding future item creation.
Conclusion: Findings revealed a predominance of knowledge-oriented cognitive content with limited skill-based, ethical, and communicative coverage. Nursing-tailored AI/data literacy instruments are urgently needed, incorporating clinical scenarios, ethics, and evolving health systems, and must be rigorously tested with nurses to ensure relevance and practicality.
Description
This study mapped 475 items from 24 AI/data literacy scales to four frameworks representing data processes and competencies. Results showed limited nursing-specific coverage and underrepresentation of skill-based, ethical, and communicative literacy. This synthesis provides a foundation for developing comprehensive, adaptive AI/data literacy instruments designed for nursing education, practice, and professional development.