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Background: Since the release of ChatGPT in November 2022, this emerging technology has garnered a lot of attention in various fields, and nursing is no exception. However, to date, no study has comprehensively summarized the status and opinions of using ChatGPT across different nursing fields.
Objective: We aim to synthesize the status and opinions of using ChatGPT according to different nursing fields, as well as assess ChatGPT's strengths, weaknesses, and the potential impacts it may cause.
Methods: This scoping review was conducted following the framework of Arksey and O'Malley and guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). A comprehensive literature research was conducted in 4 web-based databases (PubMed, Embase, Web of Science, and CINHAL) to identify studies reporting the opinions of using ChatGPT in nursing fields from 2022 to September 3, 2023. The references of the included studies were screened manually to further identify relevant studies. Two authors conducted studies screening, eligibility assessments, and data extraction independently.
Results: A total of 30 studies were included. The United States (7 studies), Canada (5 studies), and China (4 studies) were countries with the most publications. In terms of fields of concern, studies mainly focused on "ChatGPT and nursing education" (20 studies), "ChatGPT and nursing practice" (10 studies), and "ChatGPT and nursing research, writing, and examination" (6 studies). Six studies addressed the use of ChatGPT in multiple nursing fields.
Conclusions: As an emerging artificial intelligence technology, ChatGPT has great potential to revolutionize nursing education, nursing practice, and nursing research. However, researchers, institutions, and administrations still need to critically examine its accuracy, safety, and privacy, as well as academic misconduct and potential ethical issues that it may lead to before applying ChatGPT to practice.
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http://dx.doi.org/10.2196/54297 | DOI Listing |
Qual Health Res
September 2025
Department of Nursing and Health Promotion, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway.
The launch of ChatGPT in November 2022 accelerated discussions and research into whether base large language models (LLMs) could increase the efficiency of qualitative analysis phases or even replace qualitative researchers. Reflexive thematic analysis (RTA) is a commonly used method for qualitative text analysis that emphasizes the researcher's subjectivity and reflexivity to enable a situated, in-depth understanding of knowledge generation. Researchers appear optimistic about the potential of LLMs in qualitative research; however, questions remain about whether base models can meaningfully contribute to the interpretation and abstraction of a dataset.
View Article and Find Full Text PDFJ Prof Nurs
September 2025
Kocaeli University of Health and Technology, Information Systems Engineering Deparment, Kocaeli, Turkey; Wefi Games Software Company, Goller Bolgesi Teknokenti, Isparta, Turkey.
Background: Comprehensive history-taking is crucial for patient assessment, prioritisation of care, and planning of care. While direct instruction methods effectively explain history-taking processes and components, they provide insufficient opportunities for practice, necessitating the implementation of supplementary teaching strategies.
Objective: This study aimed to examine the effects of AI chatbot-supported history-taking training on nursing students' questioning skills and clinical stress levels.
Nurse Educ Pract
September 2025
School of Nursing, Anhui Medical University, No.81 Meishan Road, Shushan District, Hefei, Anhui 230032, PR China; Department of Nursing, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Shushan District, Hefei, Anhui 230022, PR China. Electronic address:
Aims: This study aimed to explore the effects of interactive teaching strategies based on generative artificial intelligence (GenAI) under the guidance of outcome-based education (OBE) theory on higher-order thinking skills (HOTS) and artificial intelligence (AI) literacy of undergraduate nursing students.
Background: Recently, GenAI-assisted teaching has been widely recognised as a trend in nursing education reform. HOTS and AI literacy are important for nursing students in the era of artificial intelligence.
Nurse Educ Pract
September 2025
University of Exeter, Interim Head, Academy of Nursing, Exeter, United Kingdom.
Aim: This study aims to assess the acceptance of a VR-based disaster emergency nursing escape room teaching method among nurses and midwives and to explore the main factors influencing their acceptance.
Background: The increasing frequency of natural disasters due to global climate change poses a significant threat to human health. Effective training for nurses and midwives is critical as they are frontline responders in disaster relief.
Nurse Educ Pract
August 2025
Department of Nursing, Mokpo National University, Muan-gun, Jeollanam-do, Republic of Korea. Electronic address:
Aim: This study aims to develop and validate an instructional debriefing model that combines question-centered learning methodology with AI prompt engineering techniques for nursing simulations.
Background: Integrating artificial intelligence (AI)-based prompt engineering into nursing simulation offers structured strategies to enhance clinical reasoning. However, current debriefing models insufficiently incorporate AI methodologies such as question-centered learning and prompt engineering, indicating a lack of theoretical and procedural frameworks METHODS: The model was developed using a four-phase approach: (1) literature review, (2) instructor interviews, (3) expert validation and (4) external evaluation of effectiveness.