Toward amplifying the good in nursing education: A quality improvement study on implementing artificial intelligence-based assistants in a learning system
- PMID: 40663996
- DOI: 10.1016/j.outlook.2025.102483
Toward amplifying the good in nursing education: A quality improvement study on implementing artificial intelligence-based assistants in a learning system
Abstract
Effective integration of artificial intelligence-based tools into nursing care and science will depend on aligned integration in nursing education. Our quality improvement study documents the process and short-term outcomes of introducing a generative AI-based tool into a nursing education system. Nursing school faculty and staff at one private, southeastern university (n = 364) piloted an internally constrained chatbot system for 2 months in 2024. Data were captured to evaluate the (a) costs of implementation, (b) use cases in nursing education, and (c) projected system impact. Costs were lower than $2 per month, per user. There were 148 diverse case reports from 35 unique users. On a separate survey, 35 respondents rated technology acceptability as 5.2/7.0. Projected impact is high (6.3/7.0), but not entirely positive (5.9/7.0). Benefits and challenges were identified. Nursing will need to invest expert time and community resources to evolve education systems along with these evolving technologies.
Keywords: Artificial intelligence; Change management; Generative AI; Innovation; Interprofessional education; Large language models; Leadership; Nursing education; Quality improvement; Systems thinking.
Copyright © 2025 The Author(s). Published by Elsevier Inc. All rights reserved.
Conflict of interest statement
Declaration of Competing Interest Authors White and Karns have a financial interest in the Amplify system as a software invention. All authors worked together on the conceptualization and implementation of this quality improvement study. To mitigate potential conflict of interest, authors Russell and Sengstack led the analysis and made final determinations regarding interpretations and presentation of findings in the paper. Regina Russell’s research is primarily funded by Vanderbilt University School of Nursing. During this study, she received effort support from the Health Resources Services Administration as the evaluator for a workforce development grant in nurse midwifery education. She also received internal funding for generative artificial intelligence research.
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