Cited by 6Artificial Intelligence in EducationAcademic integrity and plagiarismArtificial Intelligence in Healthcare and Education

Artificial intelligence in academic writing: a detailed examination

Scott-Herring, Mary·2024·International Journal of Nursing Education Scholarship

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

Crossref · 10.1515/ijnes-2024-0050

Cites

3 of 3 references verified0 citation errors · 0 not checkable

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Authors
Scott-Herring, M.
Affiliations
Georgetown University (US)
Corresponding author
Scott-Herring, Mary
Venue
International Journal of Nursing Education Scholarship
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

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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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