Educators' Perspectives on Generative <scp>AI</scp> Use in Nursing Education: Friend or Foe?

AIM: This study explores nursing educators' perspectives on the challenges and benefits of integrating generative artificial intelligence (AI) into nursing education. There is little empirical evidence on how educators perceive these technologies and how such perceptions influence their integration into curriculum design, teaching practices, assessment, and student research and learning. DESIGN: Exploratory-descriptive qualitative study underpinned by the Actor-Network Theory. METHODS: Four focus group sessions were conducted with ten nursing educators across Australia, New Zealand, Austria and Hong Kong. Data were collected via Zoom, transcribed verbatim, and analysed thematically using Braun and Clarke's six-step reflexive framework. RESULTS: The nurse educators included five women and five men. Ages ranged between 25 and 64 years and a mean 8.75 [SD ±6.36] years of experience as an educator. Three main themes were constructed based on focus group discussions: (1) The AI Dilemma, revealing tensions surrounding academic integrity, policy ambiguity and ethical concerns; (2) The AI Toolkit, identifying pedagogical benefits alongside challenges to critical thinking development; and (3) Educator's AI Odyssey, exposing disparities in institutional preparedness and educator competence. Whilst AI was recognised for enhancing engagement and efficiency, substantive concerns persisted regarding equity, ethical implementation and organisational readiness. CONCLUSION: Generative AI presents a paradox in nursing education. Whilst it enables innovation and personalised learning, it poses risks to academic integrity and deep learning when implementation lacks ethical consideration and pedagogical rigour. IMPLICATIONS FOR PROFESSION AND PATIENT CARE: AI-enhanced nursing education must safeguard fundamental nursing values, critical thinking capabilities, ethical reasoning and clinical judgement to ensure the delivery of safe, competent patient care. IMPACT: Educational and institutional policies must facilitate balanced, ethical and equitable integration of AI in nursing education. REPORTING METHOD: The Consolidated Criteria for Reporting Qualitative Research (COREQ). PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

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Publication Details

Journal
Journal of Clinical Nursing
Published
2026-07-30
DOI
https://doi.org/10.1111/jocn.70477
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00

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article

Educators' Perspectives on Generative AI Use in Nursing Education: Friend or Foe?

Frank Donnelly, Clare Walters, Peta Drury, Belinda McGrath et al.
Journal of Clinical Nursing
Artificial Intelligence in Healthcare and Education
article

Educators' Perspectives on Generative AI Use in Nursing Education: Friend or Foe?

Frank Donnelly, Clare Walters, Peta Drury, Belinda McGrath, Manela Glarcher, Yenna Salamonson, Lucie M. Ramjan, Gina Richards
article en

Abstract

AIM: This study explores nursing educators' perspectives on the challenges and benefits of integrating generative artificial intelligence (AI) into nursing education. There is little empirical evidence on how educators perceive these technologies and how such perceptions influence their integration into curriculum design, teaching practices, assessment, and student research and learning. DESIGN: Exploratory-descriptive qualitative study underpinned by the Actor-Network Theory. METHODS: Four focus group sessions were conducted with ten nursing educators across Australia, New Zealand, Austria and Hong Kong. Data were collected via Zoom, transcribed verbatim, and analysed thematically using Braun and Clarke's six-step reflexive framework. RESULTS: The nurse educators included five women and five men. Ages ranged between 25 and 64 years and a mean 8.75 [SD ±6.36] years of experience as an educator. Three main themes were constructed based on focus group discussions: (1) The AI Dilemma, revealing tensions surrounding academic integrity, policy ambiguity and ethical concerns; (2) The AI Toolkit, identifying pedagogical benefits alongside challenges to critical thinking development; and (3) Educator's AI Odyssey, exposing disparities in institutional preparedness and educator competence. Whilst AI was recognised for enhancing engagement and efficiency, substantive concerns persisted regarding equity, ethical implementation and organisational readiness. CONCLUSION: Generative AI presents a paradox in nursing education. Whilst it enables innovation and personalised learning, it poses risks to academic integrity and deep learning when implementation lacks ethical consideration and pedagogical rigour. IMPLICATIONS FOR PROFESSION AND PATIENT CARE: AI-enhanced nursing education must safeguard fundamental nursing values, critical thinking capabilities, ethical reasoning and clinical judgement to ensure the delivery of safe, competent patient care. IMPACT: Educational and institutional policies must facilitate balanced, ethical and equitable integration of AI in nursing education. REPORTING METHOD: The Consolidated Criteria for Reporting Qualitative Research (COREQ). PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Journal of Clinical Nursing
Statistics New Zealand (NZ), Murdoch University (AU), University of Wollongong (AU), Paracelsus Medical University (AT), Lishman Health Foundation (AU), Te Pūkenga (NZ), Western Sydney University (AU)
Western Sydney University
Openalex Percentile: Top 12%
Artificial Intelligence in Healthcare and Education
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