Aug 2026· Indonesian Contemporary Nursing Journal (ICON Journal)· 0 citations· 37 references
TL;DR
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.
Abstract
Aims: This study aimed to synthesize the scientific literature on the integration of artificial intelligence (AI) into nursing education to significantly enhance learning outcomes. The application of AI in clinical teaching can enhance nursing students' preparation for a technologically advanced healthcare environment.
Methods: This study used a narrative literature review. Key electronic databases, including CINAHL, MEDLINE, Scopus, and Google Scholar, were searched according to the PRISMA guidelines. The review included articles published between 2020 and 2024, written in English, and employing qualitative and quantitative research designs. The search items included AI, ChatGPT, challenges, opportunities, nursing education, technology, students, teaching, and learning. Data were synthesized by summarizing the main results of the included studies.
Results: Ten studies met the inclusion criteria and were included in the review. The findings showed that AI can enhance clinical teaching, improve nursing students' self-efficacy, and support teaching and learning. However, challenges related to academic integrity, assessment quality, unequal access to AI, and inadequate skill development were also identified.
Conclusion: The findings of this study revealed that the use of AI in nursing education is instrumental in improving the acquisition of clinical skills and teaching and learning. Nursing education institutions should create awareness of the safe use of AI. Furthermore, policies should be implemented to ensure that AI use is controlled and adequately monitored. All stakeholders, including patients, students, nurses, and nurse educators, should be developed and provided with adequate resources for effective AI implementation.
Aim: This meta-analysis examined the effectiveness of artificial intelligence (AI)-based educational interventions on learning outcomes in nursing students.Material and Method: A systematic search of PubMed, Scopus, CINAHL, and Web of Science identified studies published between January 2021 and May 2025. Following PRISMA guidelines, 11 studies with 1,539 students were included, comprising randomized controlled trials and quasi-experimental designs. Outcomes assessed were knowledge, clinical reasoning, satisfaction, clinical performance, attitude, and self-efficacy. Risk of bias was evaluated with the RoB 2 tool, and random-effects models were used for meta-analysis.Results: AI-based interventions significantly improved knowledge acquisition (MD = 4.47, 95% CI [2.60, 6.34], p
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
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
The evidence showed that, virtual reality simulations with varying immersive abilities were the most popular innovative educational technology used in undergraduate mental health nursing education and both immersive and non‐immersive VRS were equally effective in improving student learning experiences compared to traditional learning modalities.
Sini Jacob, Sebastian Samuel, Greety Antony et al.· International Journal of Men...· 0 citations
Nursing education is very crucial in training qualified healthcare practitioners who can address the needs of the contemporary healthcare systems. As teaching methodologies have developed, several teaching strategies including traditional teaching in the classroom, learning through simulations, e-learning, and blended learning have become popular. Nevertheless, the success of these programs is inconsistent in various studies which makes it necessary to conduct an overall assessment. To examine the effectiveness of nursing education programs on a large scale, this research lies on secondary data gathered in the form of published research articles, journals and reports of reputable databases, which include PubMed, Google Scholar and World Health Organization (WHO). The research is directed at comparing the learning outcomes, clinical skills development, and student satisfaction in the various teaching methods. The results show that technology and modern techniques of learning, especially simulation and blended learning are more effective than the traditional techniques. Statistical analysis shows that there is a great enhancement in student performance and acquisition of skills in different studies. The findings also indicate the differences in effectiveness based on the nature of educational intervention applied. The research concludes that education programs in nursing that include new and digital teaching methods have a great impact on the results of education. It highlights the importance of ongoing improvement, introduction of the latest technologies, and evidence-based teaching to provide high-quality nursing education.
Balraj Sharma, Sravanthi Mettu, Princia Rolita Dsousa et al.· International Journal of Res...· 0 citations
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.
Min-Qi Xia, Qingqing Zhu, Yaxuan Luo et al.· Nurse Education in Practice· 0 citations