Cited by 21Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationEducational Technology and Pedagogy

Beyond hype: Is ChatGPT-generated content effective in class preparation among academic instructors?

Bin-Nashwan, Saeed Awadh, Bouteraa, Mohamed, Benlahcene, Abderrahim, Sadallah, Mouad·2025·The Internet and Higher Education

Integrity signals

Registry

Crossref · 10.1016/j.iheduc.2025.101016

Cites

108 of 108 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Cites

1 retracted

Flagged first, then by citations

1 of the 109 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Authors
Bin-Nashwan, S. A., Bouteraa, M., Benlahcene, A., & Sadallah, M.
Affiliations
  • Qatar Foundation (QA)
  • Ajman University (AE)
  • University of Ha'il (SA)
Venue
The Internet and Higher Education
Publisher
Elsevier BV
Published
2025-06
Volume
66
Pages
101016
ISSN
1096-7516 (ISSN-L)
OpenAlex
W4409727236
Cited by
21
References
109
Citations by year
2026: 17 · 2025: 4
FWCI
22.02
Type
journal article
Language
en
Open access
closed
Locations
The Internet and Higher Education (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Educational Technology and Pedagogy
Keywords
ChatGPT, AI-generated content, lesson planning, instructor confidence, work intensity, institutional culture, training and support, university teaching, artificial intelligence in education

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.

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