Aug 2026· Nurse Education Today· Vol 168, pp.
107354
· 0 citations· 23 references
Medicine
TL;DR
It is suggested that structured AI-comparison tasks offer the opportunity to develop AI and digital health literacy in nursing students and offer practical guidance for educators seeking to support graduates to be AI-critical and well-equipped to leverage the efficiencies of these tools.
Abstract
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 Students identified fabricated references, outdated information, and absence of peer-reviewed sourcing as key limitations.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: The integration of Evidence-Based Practice (EBP) into nursing education faces challenges in linking theory to clinical application in complex family health contexts. Students struggle with efficiently accessing, appraising, and applying evidence influenced by sociocultural factors. Artificial intelligence (AI) offers transformative potential but requires pedagogical design to foster critical thinking and ethical use beyond technical skills.Method: An action research with mixed methods was conducted with 100 nursing students. The intervention had four phases: participatory family diagnosis, AI-assisted evidence retrieval and validation, community educational workshops design and execution, and multi-level evaluation.Results: A significant shift in AI use from basic to strategic, with a 40% reduction in literature search time. Qualitative data revealed enhanced critical awareness and ethical reasoning, while quantitative results indicated 90% of students improved critical appraisal skills and 70% felt more confident in evidence-based decisions. The project impacted 100 families, with 90% trusting evidence-based recommendations.Conclusions: Integrating AI in experiential pedagogies like Design Thinking and Service-Learning effectively develops nursing competencies, ensuring technology adoption supports context-sensitive family health learning outcomes.
Maria Graciela Villalba-Condori, Carla Cuya-Zevallos· Publicaciones· 0 citations
BACKGROUND
Generative AI (GenAI) is rapidly reshaping nursing education, yet its impact on students' clinical judgment and ethical reasoning remains multifaceted and complex.
OBJECTIVES
To synthesize qualitative evidence exploring nursing students' experiences with GenAI in developing core clinical and ethical competencies.
REVIEW METHODS
A systematic meta-synthesis was conducted following ENTREQ guidelines. Five major databases (Cochrane, CINAHL, PubMed, Web of Science, and Embase) were searched up to February 2026. Data were synthesized using Thomas and Harden's thematic synthesis approach, and confidence in the findings was assessed with GRADE-CERQual.
RESULTS
Eleven studies were included, yielding four meta-themes: (1) personalized support versus the risk of dependency, (2) developing clinical judgment through simulation and verification, (3) ethical reasoning and the human element, and (4) the need for institutional guidance and literacy.
CONCLUSIONS
Nursing students use GenAI for support, simulation, and feedback, but they also report risks of over-reliance, weak verification, and unclear ethical responsibility. Nursing programmes should teach students how to question AI outputs, protect patient data, and retain human clinical judgment when using GenAI.
PROSPERO ID
The review was registered on PROSPERO (registration number: CRD420261291252).
Jing Tian, Xing-Rong Shen, Nan Li et al.· Nurse Education Today· 0 citations
INTRODUCTION
Reflection, a cornerstone of professional development and a core component of medical education, is increasingly challenged by the emergence of generative artificial intelligence (AI). AI's capability to mimic human behaviour through real-time feedback, content generation, and conversational interfaces presents pressing ethical and pedagogical concerns regarding its use in reflection.
AIM
This qualitative study sought to explore the perspectives of educators on undergraduate medical students using AI in reflection.
METHODS
Data were generated through two online focus groups with general practitioners in the role of expert participants, each lasting around one hour and including three participants. All six participants were academic undergraduate primary care educators who teach reflection skills to students.
RESULTS
Five themes were conceptualised by reflexive thematic analysis: questioning assessment of reflection; professionalism in jeopardy; acceptability of using AI in learning; digital divide and educational equity; and institutional and educator readiness for AI. Educators faced complex tensions between embracing technological progress and protecting the relational and ethical foundations of medical education.
DISCUSSION
Although there was cautious optimism about AI's role as a facilitative tool, participants uniformly emphasised that its educational value depends on critical, transparent, and ethically grounded implementation. This study highlights how undergraduate primary care educators should be cautious of the heightened challenges to academic integrity posed by AI. Uncertainty around fostering authentic student engagement in reflection suggests the need for further exploration in partnership with students. These findings highlight key areas for medical educators to support learners' early engagement with AI.
Shabana Bharmal, Erik Blair, Michael Page· Journal of Primary Health Ca...· 0 citations
Embedding structural changes within assessment design, rather than relying on rule enforcement, will ensure nursing and midwifery graduates are prepared to thrive in an AI-enabled world.
Y. Salamonson, Pauletta Irwin, R. Kornhaber et al.· Journal of Clinical Nursing· 0 citations
The impact of transitioning from a traditional to an AI-aware rubric in a Health and Medicine course is evaluated, examining how this shift influenced educators’ grading practices, instructional strategies, and perceptions of student engagement.
Suzanne Estaphan, Tehzeeb Zulfiqar· Frontiers in Education· 0 citations
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, C. Walters et al.· Journal of Clinical Nursing· 0 citations
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