Chatting or cheating? The impacts of ChatGPT and other artificial intelligence language models on nurse education
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Restrictive approaches limit GenAI use, particularly in assessments, emphasising verification requirements and traditional skill development.
“Restrictive approaches limit GenAI use, particularly in assess ments, emphasising verification requirements and traditional skill development”
From Patterns, advances, and gaps in using ChatGPT and similar technologies in nursing education: A PAGER scoping review (2025) · cites this paper as Choi et al., 2023
Nursing professionals are challenged to quickly adapt to evolving medical knowledge, make accurate decisions in real-case scenarios, and master complex nursing skills.
“Additionally, with the rapid advancements in nursing research and the advent of new diseases and medications, nursing professionals are challenged to quickly adapt to evolving medical knowledge, make accurate decisions in real-case scenarios, and master complex nursing skills (1, 17, 26–28).”
From Digital affordances of AI chatbots in nursing education: a systematic review of learning gains and gaps in the evidence (2026) · cites this paper as [1]
Generative AI's potential to foster misinformation, over-reliance, and reduced critical judgment could undermine the ethical foundations of nursing education if left unchecked.
“Scholars caution that generative AI’s potential to foster misinformation, over-reliance, and reduced critical judgment could undermine the ethical foundations of nursing education if left unchecked [12].”
From From adoption to interaction: examining human-GenAI engagement patterns, ethical tensions, and educational trade-offs in nursing education (2026) · cites this paper as [12]
Professional behavioral learning in nursing requires students to engage in hands-on practice in authentic or simulated clinical environments, mastering various nursing skills through practical operation and repeated training, rather than relying solely on dialogue interactions with AI chatbots.
“professional behavioral learning in nursing requires students to engage in hands-on practice in authentic or simulated clinical environments, mastering various nursing skills through practical operation and repeated training, such as patient care, surgical nursing, and intravenous infusion, rather than relying solely on dialogue interactions with AI chatbots (1).”
From Digital affordances of AI chatbots in nursing education: a systematic review of learning gains and gaps in the evidence (2026) · cites this paper as [1]
Numerous researchers forecasted how ChatGPT will affect the educational system (12 publications).
“Numerous researchers have forecasted how ChatGPT will affect various areas, including the educational system Choi et al. (2023), Lim et al. (2023) (12 publications).”
From Decoding ChatGPT: A taxonomy of existing research, current challenges, and possible future directions (2023) · cites this paper as Choi et al. (2023)
Concerns have been raised that ChatGPT may disseminate false information and references, reinforce bias, compromise academic integrity, and cause potential decline in students' skills.
“These include the dissemination of false information and references [12, 23, 61, 85], biased reinforcement [47, 50], compromised academic integrity [18, 40, 66, 74], and the potential decline in students' skills [43, 61, 64, 74].”
From A systematic literature review of empirical research on ChatGPT in education (2024) · cites this paper as [12]
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Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT
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Overview of ChatGPT Model Architecture
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- Authors
- Choi, E. P. H., Lee, J. J., Ho, M.-H., Kwok, J. Y. Y., & Lok, K. Y. W.
- Affiliations
- University of Hong Kong (HK)
- Corresponding author
- Choi, Edmond Pui Hang
- Venue
- Nurse Education Today
- Publisher
- Elsevier BV
- Published
- 2023-06
- Volume
- 125
- Pages
- 105796
- ISSN
- 0260-6917 (ISSN-L)
- OpenAlex
- W4324364344
- Semantic Scholar
- 77a3a61e96029a7c0d29133a082d814ac79d4430
- Cited by
- 172
- References
- 12
- Citations by year
- 2026: 27 · 2025: 44 · 2024: 60 · 2023: 41
- FWCI
- 40.37
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nurse Education Today (publishedVersion)
- PubMed (publishedVersion)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- ChatGPT, generative AI, nursing education, university students, technology acceptance model, effort expectancy, performance expectancy, intention to use, knowledge sharing, academic dishonesty, AI adoption in education
- MeSH terms
- Artificial Intelligence, Education, Nursing, Educational Status, Humans, Language
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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