Aug 2026· Nurse Education in Practice· Vol 96, pp.
104956
· 0 citations· 29 references
Medicine
TL;DR
Preliminary evidence suggests that AI literacy educational interventions may improve nursing students' AI literacy, and future research should prioritize well-designed randomized controlled trials, the development of nursing-specific AI literacy assessment instruments and investigations of long-term outcomes to inform educational practices.
Abstract
Aim
To systematically synthesize evidence on AI literacy educational interventions among nursing students.
Background
Artificial Intelligence (AI) literacy is increasingly recognized as an important educational priority for nursing students. However, evidence regarding educational interventions designed to enhance AI literacy remains limited and fragmented.
Design
A systematic review.
Methods
Seven databases were searched from inception to March 24, 2026. Studies were included if they: (1) enrolled nursing students as participants; (2) evaluated AI literacy educational interventions; (3) measured AI literacy as an outcome; and (4) employed quasi-experimental or randomized controlled trial designs. Methodological quality was assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies and findings were synthesized narratively.
Results
Three quasi-experimental studies involving 443 nursing students were included. Two studies included comparison groups, whereas one used a single-group pre-test/post-test design. All three studies reported statistically significant improvements in AI literacy following the interventions. One study reported significant improvements in higher-order thinking skills, another reported a significant reduction in AI anxiety and the third demonstrated enhanced performance in academic writing. Methodological quality was rated as high in two studies and moderate in one study. Heterogeneity in study designs and measurement instruments precluded meta-analysis.
Conclusions
Preliminary evidence suggests that AI literacy educational interventions may improve nursing students' AI literacy. However, the current evidence base remains limited, with only three quasi-experimental studies identified and no randomized controlled trials available. Future research should prioritize well-designed randomized controlled trials, the development of nursing-specific AI literacy assessment instruments and investigations of long-term outcomes to inform educational practices.
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
This focused conceptual narrative review synthesized literature on AI literacy and related constructs in undergraduate medical education, using a structured search and interpretive synthesis with competency-based medical education (CBME) as an interpretive lens to identify recurring domains.
Chao Fu, Jing-Jing Li, Haoyi Fan et al.· Frontiers in Medicine· 0 citations
A meta-analysis examined the effectiveness of artificial intelligence (AI)-based educational interventions on learning outcomes in nursing students and found that AI-based interventions significantly improved knowledge acquisition.
Orkun Erkayıran· Bandırma Onyedi Eylül Üniver...· 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.
Although baseline knowledge of AI among medical students and faculty members was limited, both groups demonstrated strong positive attitudes and a clear demand for further training, highlighting the importance of integrating structured AI education into medical curricula to support the responsible and effective use of emerging technologies.
Ay Sıla Çaloğlu, Halid Durna, Zeynep Naz Ergen et al.· Journal of Medical Education...· 0 citations
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