ChatGPT integration within nursing education and its implications for nursing students: A systematic review and text network analysis

Objective: This study aimed to explore prevalent topics related to integrating ChatGPT into nursing education, specifically focusing on its impact on nursing students. Design: A systematic review and text network analysis were conducted. Data sources: Full-text articles from reputable scientific databases, including PubMed, Scopus, and ScienceDirect, along with relevant articles identified through Google Scholar, were utilized, and the search period ranged from January 20th to January 23rd, 2024. Review methods: The review centered on the main text of the articles, which were initially converted from PDFs to TXT files. Text cleaning and preprocessing were conducted using Python 3.11, followed by text analysis performed with InfraNodus. Results: Of 145 articles, 46 full-text articles were included in the final analysis. Four key topical clusters were identified: Academic Writing, Healthcare Simulation, Data Modeling, and Personal Development. Sentiments regarding the integration of ChatGPT into nursing education and its impact on nursing students were primarily positive (48 %), with a notable percentage expressing negative views (31 %) and a smaller proportion indicating neutrality (21 %). Conclusion: This study highlights the transformative potential of ChatGPT in nursing education, advocating for its responsible integration to empower nursing students with advanced skills and uphold ethical standards in artificial intelligence (AI) utilization. The identified four topics and the sentiments are crucial for guiding educators, researchers, and practitioners in nursing education to navigate the integration of AI tools effectively. Further research and exploration in these areas can contribute to the ongoing discourse on the intersection of technology and nursing education.