Nursing Students’ Perceptions and Use of Generative Artificial Intelligence in Nursing Education
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Background/Objectives: Artificial intelligence (AI) is transforming nursing, with generative AI (GenAI) tools such as ChatGPT offering opportunities to enhance education through personalized learning pathways. This study aimed to explore nursing students’ use of generative artificial intelligence (GenAI) and their perceptions of its use in nursing education, including its advantages, disadvantages, and perceived support needs. Methods: This study employed an online survey. The participants were 99 undergraduate nursing students in New York City. Data was collected online through self-report measures using semi-structured, open-ended questions. The data was analyzed using content analysis. Results: Most participants (92%) used GenAI tools to access accurate information, clarify nursing concepts, and support clinical tasks such as diagnoses and health assessments, as well as schoolwork, grammar checks, and health promotion. They valued GenAI as a quick, accessible resource that simplified complex information and supported learning through definitions, practice questions, and writing improvements. However, the participants noted drawbacks, such as subscription costs, over-reliance, information overload, and accuracy issues, leading to trust concerns. The participants suggested financial support, early guidance, and instructional modules to better integrate AI into nursing education. Conclusions: The results indicate that GenAI positively impacts nursing education and highlight the need for guidelines on critical evaluation. To integrate GenAI effectively, educators should consider introductory sessions, support programs, and a GenAI-friendly environment, promoting responsible AI use and preparing students for its application in nursing education.
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Knowledge about AI risks does not automatically translate into responsible interaction practices.
“The difference between awareness and critical engagement suggests that knowledge about AI risks does not automatically translate into responsible interaction practices, reinforcing arguments for stronger AI literacy and pedagogical scaffolding [6, 17, 28].”
From From adoption to interaction: examining human-GenAI engagement patterns, ethical tensions, and educational trade-offs in nursing education (2026) · cites this paper as [28]
Results §3.4 reports that nursing students recognized risks including inaccuracy, bias, and plagiarism. Discussion §4.2 calls for critical evaluation, ethical use, AI literacy, and clear guidelines. The paper does not establish a gap between students’ risk awareness and their actual interaction practices, so that part of the claim remains an inference. (LLM, conf=0.82)
Experimental — accuracy not yet measuredMore than 80% of university students have used generative AI tools for academic purposes.
“Recent international surveys indicate that more than 80% of university students have used generative AI tools for academic purposes”
From From adoption to interaction: examining human-GenAI engagement patterns, ethical tensions, and educational trade-offs in nursing education (2026) · cites this paper as [28]
Results §3.2 reports that 92% of participants used GenAI and/or assistive AI tools at least once during the semester, and describes academic uses. But Methods §2.2 identifies the participants as 99 nursing students at one New York City college; the 92% figure does not establish that more than 80% of university students internationally used generative AI specifically for academic purposes. (LLM, conf=0.88)
Experimental — accuracy not yet measuredHan et al. (2025) found that 92% of surveyed nursing students (n = 99) had used generative AI tools, primarily for concept clarification and clinical knowledge acquisition.
“92% of surveyed nursing students (n = 99) had used generative AI tools, primarily for concept clarification and clinical knowledge acquisition”
From When the algorithm enters the classroom: A critical integrative review of large language models, nursing education structural gaps, and the reconstitution of professional identity (2026) · cites this paper as Han et al. (2025)
Han et al. (2025) identified the subscription cost of paid-tier AI tools as an access barrier.
“identified the subscription cost of paid-tier AI tools as an access barrier.”
From When the algorithm enters the classroom: A critical integrative review of large language models, nursing education structural gaps, and the reconstitution of professional identity (2026) · cites this paper as Han et al. (2025)
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- Authors
- Han, S., Kang, H. S., Gimber, P., & Lim, S.
- Affiliations
- LaGuardia Community College (US)
- Chung-Ang University (KR)
- Columbia University Irving Medical Center (US)
- Corresponding author
- Lim, Sunghyun
- Venue
- Nursing Reports
- Publisher
- MDPI AG
- Published
- 2025-02-14
- Volume
- 15
- Issue
- 2
- Pages
- 68
- ISSN
- 2039-439X (ISSN-L)
- 2039-4403
- PMC
- PMC11858139
- OpenAlex
- W4407584988
- Cited by
- 86
- References
- 24
- Citations by year
- 2026: 66 · 2025: 20
- FWCI
- 20.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nursing Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- CUNY Academic Works (City University of New York) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Health Professions › General Health Professions › Health Sciences Research and Education
- Keywords
- generative AI, ChatGPT, nursing students, nursing education, student perceptions, AI use in learning, overreliance on AI, information overload, trust in AI, AI integration in curriculum, responsible AI use
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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