Jul 2026· Journal of Clinical Nursing· 0 citations· 30 references
Medicine
TL;DR
Generative AI presents a paradox in nursing education as it enables innovation and personalised learning, but poses risks to academic integrity and deep learning when implementation lacks ethical consideration and pedagogical rigour.
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.
BACKGROUND
The rapid integration of generative artificial intelligence (GenAI) into nursing education presents both opportunities and challenges, yet empirical evidence on students' critical engagement with AI-generated content within assessment contexts remains limited.
AIM
To examine undergraduate nursing students' reflections when comparing their own evidence-based summaries with AI-generated outputs in response to the same clinical research questions.
METHODS
A qualitative descriptive design was employed using retrospective analysis of 497 assessment submissions from an undergraduate nursing cohort at an Australian university. Students formulated a research question, synthesised peer-reviewed evidence, submitted the same question to an AI tool, and critically reflected on the comparison. Data were analysed using qualitative content analysis and thematic analysis.
RESULTS
Four themes were identified: 1. Credibility, quality of evidence and academic rigour. Students identified fabricated references, outdated information, and absence of peer-reviewed sourcing as key limitations. Additionally, students reflected on algorithmic limitations and the challenge of verifying AI outputs without prior topic knowledge; 2. Critical thinking, depth of analysis, and human intelligence. AI was perceived as unable to replicate contextual reasoning or multi-source synthesis; 3. Efficiency, accessibility, and practical utility. AI's speed and clarity were valued for brainstorming and initial scoping; and 4. Student identity, learning, and professional development were shaped by the view that engaging in manual, hands-on research was integral to forming a safe, evidence-informed nursing identity.
CONCLUSION
This study suggests that structured AI-comparison tasks offer the opportunity to develop AI and digital health literacy in nursing students. Students are neither naively accepting of AI nor reflexively dismissive but are actively working to understand its place within the ethical frameworks of nursing education. These findings contribute to AI integration in nursing education and offer practical guidance for educators seeking to support graduates to be AI-critical and well-equipped to leverage the efficiencies of these tools.
INTRODUCTION
This study explored clinical educators' perspectives on their supervision experiences, focusing on successes and challenges, including experiences working with allied health students who underperformed or failed practice placements.
METHOD
This study used a convergent mixed-methods design. Universities in Australia and New Zealand (n = 19) that deliver allied health programs (n = 14 professions) were invited to participate. An online questionnaire administered to allied health clinical educators collected quantitative and qualitative data. Quantitative data were analysed descriptively, and content analysis was used to analyse free-text responses. Occupational Adaptation Theory was applied as an interpretative framework.
RESULTS
Sixteen universities consented to participate. Data from 66 questionnaires representing clinical educators from 10 professions were compared and triangulated. Constructed themes described that positive placement experiences were shaped by clinical educators' embracing their supervisory roles, having strong professional connections and supportive workplaces. In contrast, negative placement experiences were associated with students lacking foundational skills, learning under pressure, or limited professional readiness.
DISCUSSION
Clinical educators reported being equipped to recognise overt student supports but underestimated the impact that environment and educational relationships had on students' capacity to adapt or seek assistance during situations of underperformance. Applying Occupational Adaptation Theory to support data interpretation highlighted that, in addition to supervisory training, mastering their roles and having actionable strategies to support learners in difficulty, requires adequate resourcing and recognition to ensure CEs are equipped to manage every element of student learning.
Amanda Wray, S. Attrill, L. Lewis· Medical Teacher· 0 citations
The rapid uptake of generative artificial intelligence (GenAI) in higher education has increased both enthusiasm and concern. While students’ use of GenAI has been widely discussed, empirical research focusing on nurse educators’ own experiences and perceptions remains limited. This systematic review synthesizes evidence on nurse educators’ experiences of using generative artificial intelligence in teaching. A systematic literature review was conducted in accordance with PRISMA 2020 guidelines. Searches were performed in PubMed, CINAHL, Web of Science, and ERIC. Peer-reviewed empirical studies published in English were included. Two reviewers independently screened records, extracted data, and conducted quality appraisal using established tools. Due to methodological heterogeneity, results were synthesized thematically. Thirteen studies were included, representing a total of 3082 participants. Two overarching themes were identified: (1) Nurse educators’ opportunities and challenges using Generative AI in teaching, and (2) Nurse educators’ competence and ways of using Generative AI. Educators described Generative AI as a potentially valuable resource for teaching efficiency and organizational and pedagogical inspiration. They expressed concerns relating to their loss of professional roles, academic integrity, and erosion of critical thinking related to students. Experience with Generative AI, institutional position, organizational policy and support influenced educators’ attitudes, confidence, and use. The findings reveal a tension between optimism about Generative AI’s pedagogical potential and apprehension about its ethical, educational, and professional implications. Educators’ calls for clearer policies, competency development, and institutional support highlight the need for systematic capacity-building. Generative AI's value depends on educators' skills, supportive policies, and intentional use, making structured training and governance essential for integration in nurse education.
Ani Henttonen, M. Christidis, Helena Kullenberg et al.· BMC Medical Education· 0 citations
This commentary is deliberately framed as a focused extension of the original study’s individual-level findings to the institutional level, rather than a stand-alone organizational literature review, and its scope has been kept correspondingly concise.
To explore how nursing educators in Portuguese higher education institutions understand, value, and operationalize genomics within nursing education programs, a qualitative, exploratory study was conducted through online focus groups with nursing educators.
Maria João Silva, L. Guimarães, Catarina Costa et al.· International Nursing Review· 0 citations