Artificial intelligence in academic writing: a detailed examination
Abstract
Introduction As AI tools have become popular in academia, concerns about their impact on student originality and academic integrity have arisen. Methods This quality improvement project examined first-year nurse anesthesiology students' use of AI for an academic writing assignment. Students generated, edited, and reflected on AI-produced content. Their work was analyzed for commonalities related to the perceived ease of use, accuracy, and overall impressions. Results Students found AI tools easy to use with fast results, but reported concerns with inaccuracies, superficiality, and unreliable citations and formatting. Despite these issues, some saw potential in AI for brainstorming and proofreading. Implications for international audience Clear guidelines are necessary for AI use in academia. Further research should explore AI's long-term impact on academic writing and learning outcomes. Conclusions While AI tools offer speed and convenience, they currently lack the depth required for rigorous academic work.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Students are interested in using GenAI in their work for idea generation, outlining, and editing.
“Study has found students interested in using GenAI in their work for these tasks (Scott-Herring 2024, p. 6).”
From Generative Artificial Intelligence, Formative Epistemic Injustice, and Educators’ Solutions (2026) · cites this paper as Scott-Herring 2024, p. 6
Cites
Sallam, M. (2023) · Healthcare · doi:10.3390/healthcare11060887 · Cited by 3,126 · see citing works
Nazari, N., Shabbir, M. S., & Setiawan, R. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e07014 · Cited by 405 · see citing works
ChatGPT in Medical Imaging Higher Education
Currie, G., Singh, C., Nelson, T., Nabasenja, C., Al-Hayek, Y., & Spuur, K. (2023) · Radiography · doi:10.1016/j.radi.2023.05.011 · Cited by 128 · see citing works
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Generative Artificial Intelligence, Formative Epistemic Injustice, and Educators’ Solutions
Smith, S. J. (2026) · Studies in Philosophy and Education · doi:10.1007/s11217-026-10100-4 · Cited by 2 · see citing works
- Authors
- Scott-Herring, M.
- Affiliations
- Georgetown University (US)
- Corresponding author
- Scott-Herring, Mary
- ORCID
- Scott-Herring, Mary
- Venue
- International Journal of Nursing Education Scholarship
- Publisher
- Walter de Gruyter GmbH
- Published
- 2024-01-01
- Volume
- 21
- Issue
- 1
- ISSN
- 1548-923X (ISSN-L)
- 2194-5772
- OpenAlex
- W4405510215
- Cited by
- 6
- References
- 3
- Citations by year
- 2026: 4 · 2025: 2
- FWCI
- 2.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Nursing Education Scholarship (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Georgetown University
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence, academic writing, AI tools in education, academic integrity, student originality, first-year nursing students, perceived ease of use, AI accuracy, proofreading, brainstorming, AI use guidelines
- MeSH terms
- Artificial Intelligence, Education, Nursing, Baccalaureate, Female, Humans, Nurse Anesthetists, Students, Nursing, Writing, Quality Improvement
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.
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