Aug 2026· BMC Medical Education· Vol 26· 0 citations· 34 references
Medicine
TL;DR
A tension between optimism about Generative AI’s pedagogical potential and apprehension about its ethical, educational, and professional implications is revealed, highlighting the need for systematic capacity-building in nurse education.
Abstract
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.
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.
Lucie Ramjan, Belinda McGrath, Clare Walters et al.· Journal of Clinical Nursing· 0 citations
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.
Qualitative evidence suggests that registered nurses perceive GAI as a potentially supportive tool for improving efficiency, assisting clinical and research decision-making, and promoting professional development.
Yan Deng, Yidan Zhu, Jiaqi Li et al.· Frontiers in Public Health· 0 citations
Thematic analysis revealed that various factors underpinning their attitudinal, normative, and control beliefs are critical determinants of nurses' and students' overall experiences with GenAI and their intentions to use GenAI technologies.
Ming Wei Jeffrey Woo, Adrian Heng Tsai Tan· Nursing and Health Sciences· 0 citations
Nursing students’ experiences with generative AI are shaped by both the opportunities and challenges associated with its use in learning, highlighting the need for nursing educators to strengthen students’ AI literacy, critical thinking, and ethical awareness.
Shanshan Du, Sha Wang, Feng-ming Yan et al.· Frontiers in Medicine· 0 citations
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