CoChat
Sign Up
PreprintCited by 3,079Academic integrity and plagiarismArtificial Intelligence in EducationArtificial Intelligence in Healthcare and Education

The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.

Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT

Open access
Cotton, Debby, Cotton, Peter, Shipway, J. Reuben·2023·Innovations in Education and Teaching International
DOI 10.35542/osf.io/mrz8h

Preprint — published as 10.1080/14703297.2023.2190148 · Paper page

Open this paper in CoChat — free accountOpen PDF

Abstract

The use of artificial intelligence in academia is a hot topic in the education field. The use of chatAPIs and GPT-3 in higher education has the potential to offer a range of benefits, including increased student engagement, collaboration, and accessibility. However, these tools also raise a number of challenges and concerns, particularly in relation to academic honesty and plagiarism. This paper examines the opportunities and challenges of using chatAPIs and GPT-3 in higher education, with a focus on the potential risks and rewards of these tools and the ways in which universities can address the challenges they pose. The paper discusses the main features and capabilities of chatAPIs and GPT-3 and provides examples of their use in higher education. It also considers the potential for these tools to be used for academic dishonesty and the difficulties of detecting and preventing such abuses. Finally, the paper suggests a range of strategies that universities can adopt to ensure that chatAPIs and GPT-3 are used ethically and responsibly, including developing policies and procedures, providing training and support, and using a variety of methods to detect and prevent cheating.

Overview

Integrity signals

Registry

Crossref · 10.35542/osf.io/mrz8h

Cites

24 of 24 references verified0 citation errors · 0 not checkable

Statistics

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

Verification

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?

Citations

Cites

Show all 24 references

Cited by

Sorted by citations

Items 701–800 of 1,006

Related

See all

Metadata

Authors
Cotton, D., Cotton, P., & Shipway, J. R.
Affiliations
  • Plymouth Marjon University (GB)
  • University of Plymouth (GB)
Corresponding author
Cotton, Peter
ORCID
Venue
Innovations in Education and Teaching International
Publisher
Center for Open Science
Published
2023-01-10
Volume
61
Issue
2
Pages
228-239
ISSN
1470-3297 (ISSN-L)
DOI
10.35542/osf.io/mrz8h
OpenAlex
W4324046518
Cited by
3,079
References
24
Citations by year
2026: 1105 · 2025: 801 · 2024: 789 · 2023: 381
FWCI
769.67
Type
journal article
Language
en
Open access
Licence
Locations
  • Innovations in Education and Teaching International (publishedVersion, cc-by-nc-nd)
  • Plymouth Marjon University Repository (Plymouth Marjon University) (acceptedVersion)
  • PEARL (University of Plymouth) (submittedVersion)
  • EdArXiv (OSF Preprints) (submittedVersion)
  • EdArXiv (OSF Preprints) (submittedVersion, cc-by)
Topics
  • Social Sciences › Education › Academic integrity and plagiarism
  • Social Sciences › Education › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
ChatGPT, academic integrity, academic dishonesty, cheating detection, plagiarism, artificial intelligence in higher education, AI ethics, university policy

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 ›

Are you an author, or do you see a problem with this page? Request a review