Aug 2026· Nurse Education Today· Vol 167, pp.
107321
· 0 citations· 25 references
Medicine
TL;DR
Graduate nursing students viewed AI as a tool to enhance the quality and effectiveness of nursing education, while emphasizing the importance of ethical sensitivity and protection of professional identity.
Abstract
Background
Artificial intelligence (AI) is becoming an integral part of nursing education; however, the perspectives of graduate nursing students on its use remain underexplored.
Aim
This study aimed to examine graduate nursing students' views on AI in nursing education.
Methods
A qualitative phenomenological design incorporating the photovoice method was adopted. Fifteen graduate nursing students from a state university were recruited between 01 March 2025 and 30 May 2025. Data were analyzed using thematic analysis in accordance with Braun and Clarke's approach.
Results
Analysis yielded five main themes and 17 subthemes: (1) Areas of AI application in nursing education, (2) Perceived advantages of AI, (3) Perceived disadvantages of AI, (4) Recommendations for effective AI integration in nursing education, and (5) Future directions for AI in nursing education.
Conclusions
Participants viewed AI as a tool to enhance the quality and effectiveness of nursing education, while emphasizing the importance of ethical sensitivity and protection of professional identity. The use of photovoice method enriched and deepened these insights.
It is suggested that nursing students in India possess adequate knowledge about AI, indicating a positive perception that AI plays a transformative function in nursing education and practice, with a need for more focused training and integration into the curriculum.
Arul Valan, Latha S Kannan, A. Subbarayalu et al.· International Research Journ...· 0 citations
Optimists and Realists appear to actively integrate AI tools into clinical practice and examination preparation and generally perceive them as beneficial for learning outcomes, highlighting the importance of adopting differentiated pedagogical approaches rather than a one-size-fits-all curriculum.
Pelin Karataş, Demet Öztürk· Journal of Education and Res...· 0 citations
Nursing academics appear to adopt AI selectively, prioritising preservation of core professional values while embracing applications perceived to enhance, rather than replace, educational practice, providing evidence for nursing education programs globally regarding faculty development, institutional policy frameworks, and curriculum design strategies integrating technological advancement whilst maintaining person-centred values.
Natasha Hawkins, Anthea Fagan, Yumiko Coffey et al.· Journal of Advanced Nursing· 0 citations
BACKGROUND
Artificial intelligence (AI) is rapidly entering academic nursing education, yet its integration remains uneven and often lacks pedagogical guidance. Understanding how nursing educators perceive AI's role is critical to ensuring its appropriate and effective use.
AIM
This rapid review aimed to synthesize current evidence on nursing educators' perceptions of AI in academic nursing education, with a focus on identifying which educational tasks can be enhanced, replaced, or are not amenable to AI.
METHODS
A rapid review was conducted using a multimethod search strategy that combined AI-assisted semantic searching, structured database searches, targeted journal and reference list searching, and manual verification. Eligible studies reporting nursing educators' perspectives on AI in academic settings were synthesized using descriptive and directed content analyses informed by predefined domains, while remaining open to emergent themes.
RESULTS
A total of 55 studies were included in this review. Educators consistently view AI as augmenting rather than replacing faculty roles. AI is perceived as most effective in simulation-based learning and personalized tutoring, followed by feedback and assessment, curriculum design and administrative work, and academic writing and research support. Bounded tasks, including administrative drafting, grading and the summarizing of narrative data, may be partially substituted, though faculty oversight remains necessary. In contrast, relational, ethical and judgment-based domains, including empathy, moral reasoning, complex clinical judgment, hands-on clinical practice and faculty mentorship, are not considered substitutable. Workload effects are directionally mixed: AI reduces time spent on bounded tasks, but verifying its outputs generates new demands. Perceptions vary with prior exposure, which correlated with trust in AI, and with age, gender, academic rank and nationality. Key barriers include limited training, ethical concerns and infrastructure gaps.
CONCLUSION
AI's educational impact depends less on technological capability than on pedagogical design, faculty preparedness, and governance. Evidence from resource-constrained settings indicates that these preconditions are themselves unevenly distributed, highlighting the need for structured implementation strategies that address infrastructure and verification burden alongside pedagogy.
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
The findings showed that AI can enhance clinical teaching, improve nursing students' self-efficacy, and support teaching and learning and that the use of AI in nursing education is instrumental in improving the acquisition of clinical skills and teaching and learning.
S. Khunou, Carine Prinsloo· Indonesian Contemporary Nurs...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
