Sep 2026· Journal of Nursing Reports in Clinical Practice· 0 citations
TL;DR
Generative AI in nursing education is an emerging field, and future research should address policy, long-term outcomes, and institutional adoption to ensure responsible integration to ensure responsible integration.
Abstract
Generative artificial intelligence (AI), particularly large language models such as Chat Generative Pre-Trained Transformer (ChatGPT), is rapidly transforming higher education, including nursing. This study mapped global research trends on the integration of generative AI in nursing education using a bibliometric approach. Articles indexed in Scopus between 2020 and 2025 were retrieved with keywords related to generative AI, ChatGPT, and nursing education. A total of 149 English-language journal articles were analyzed, and bibliometric visualization was conducted using VOSviewer version 1.6.20 to examine publication patterns, leading authors, journals, institutions, countries, and thematic clusters. Results showed a steady rise in publications, with significant growth in 2023–2024 following the widespread adoption of ChatGPT. The most prolific author is from Taipei Medical University, while Nurse Education in Practice was the top journal with 13 articles and 113 citations. Taipei Medical University and NUS Yong Loo Lin School of Medicine were the most productive institutions, and the United States led in overall output and international collaborations. Keyword analysis revealed four thematic clusters: technological foundations, pedagogical applications, competency and critical thinking, and nursing informatics. Generative AI in nursing education is an emerging field, and future research should address policy, long-term outcomes, and institutional adoption to ensure responsible integration.
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 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
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
Keyword and thematic analyses showed that current research attention is mainly concentrated on ChatGPT, large language models, natural language processing, clinical reasoning simulation, personalized learning, virtual patient interaction, and ethical governance.
Jia Zhang, Yuanzhou Liu, Bo Wang· Medicine· 0 citations
The dual role of GenAI in HOTS development indicates that nursing education should integrate GenAI judiciously, and efforts should focus on improving the quality of human-AI collaboration, with particular attention to humanistic care, thereby helping students positively reshape their professional perceptions.
Yan Ning, Shanshan Sun, Yingkun Lu et al.· Nurse Education in Practice· 0 citations
The integration of Artificial Intelligence into individualized educational experiences represents a transformative model for medical education that enables adaptive learning pathways, dynamic assessment methods, and data-driven instructional environments, thereby enhancing student engagement, fostering faculty innovation, and promoting equity in learning outcomes.
Ava Taghavi Monfared, Maryam Hojati, Zohreh Farahmandpour et al.· Journal of Advances in Medic...· 0 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.