Cited by 5Artificial Intelligence in Healthcare and EducationInnovations in Medical EducationAcademic integrity and plagiarism

Why we should (not) worry about generative AI in medical ethics teaching

Open access
Segers, Seppe·2024·International Journal of Ethics Education

Integrity signals

Registry

Crossref · 10.1007/s40889-023-00179-5

Cites

10 of 10 references verified0 citation errors · 0 not checkable

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  • A lack of empirical research and foundational definitions of AI ethics was noted as a barrier to guiding curriculum development.

    “A lack of empirical research and foundational definitions of AI ethics was noted as a barrier to guiding curriculum development [190, 195, 198–200].”

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Authors
Segers, S.
Affiliations
Ghent University (BE)
Corresponding author
Segers, Seppe
Venue
International Journal of Ethics Education
Published
2023-10-12
Volume
9
Issue
1
Pages
57-63
ISSN
  • 2363-9997 (ISSN-L)
  • 2364-0006
OpenAlex
W4387572510
Cited by
5
References
10
Citations by year
2025: 2 · 2024: 3
FWCI
0.19
Type
journal article
Language
en
Open access
Locations
  • International Journal of Ethics Education (publishedVersion)
  • Ghent University Academic Bibliography (Ghent University) (submittedVersion, other-oa)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
  • Social Sciences › Safety Research › Academic integrity and plagiarism
Keywords
generative AI, large language models, medical ethics education, medical professionalism, moral reasoning, medical student assessment, student papers, ethical principles, group discussion

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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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