CoChat
Sign Up
Cited by 19Academic integrity and plagiarismArtificial Intelligence in EducationLegal Issues in Education

The academic integrity technological arms race and its impact on learning, teaching, and assessment

Open access
Eaton, Sarah Elaine·2023·Canadian Journal of Learning and Technology
DOI 10.21432/cjlt28388
Open this paper in CoChat — free accountOpen PDF

Abstract

This essay discusses the technological arms race that has developed in response to academic cheating. The author highlights three technological advances that impact academic integrity, from oldest to newest: a) text-matching software, b) online exam proctoring software, and c) artificial intelligence and Large Language Models (LLMs). This essay argues that there is no “silver bullet” to preventing or investigating academic misconduct and that our ethical obligations for learning, teaching, and assessment must include a human focus to promote student success.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.21432/cjlt28388

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Uncritical reliance can undermine originality, authorship norms, and institutional trust.

    “Responsible use may promote ethical reflection and conscientious academic behavior (Memarian & Doleck, 2023), yet uncritical reliance can undermine originality, authorship norms, and institutional trust (Chan, 2023;Eaton, 2022;Fowler, 2023;Grassini, 2023;Katsamakas et al., 2024;Malinka et al., 2023;Nazaretsky et al., 2025).”

    From Academic Integrity and Students’ Ethical Use of ChatGPT in Higher Education (2026) · cites this paper as Eaton, 2022

    In “Artificial Intelligence and Large Language Models” (page unavailable), Eaton warns that students might let AI do their academic writing and discusses outsourcing work as a potential academic-integrity concern. That supports a concern about originality and who produced the work, but the paper does not connect uncritical reliance to a loss of institutional trust. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Existing academic integrity frameworks primarily address traditional forms of misconduct, such as plagiarism and cheating, focusing on moral reasoning and institutional codes rather than technology-mediated ethics.

    “Existing academic integrity frameworks (e.g., Borenstein & Howard, 2021;Eaton, 2022;Macfarlane et al., 2014) primarily address traditional forms of misconduct, such as plagiarism and cheating, focusing on moral reasoning and institutional codes rather than technology-mediated ethics.”

    From Academic Integrity and Students’ Ethical Use of ChatGPT in Higher Education (2026) · cites this paper as Eaton, 2022

    The Introduction discusses plagiarism, cheating, and technologies used to detect misconduct; the Conclusions discuss ethical technology use. Neither section assesses existing academic integrity frameworks or says they prioritize moral reasoning and institutional codes over technology-mediated ethics. Searches for frameworks, moral reasoning, and policies found no passage establishing that comparison. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 18

  1. Berkey, D., & Halfond, J. (2015). Cheating, Student Authentication and Proctoring in Online Programs. New England Journal of Higher Education, 1-1.
  2. Bertram Gallant, T. (2008). Academic integrity in the twenty-first century: A teaching and learning imperative. Hoboken, NJ: Wiley.
  3. Bretag, T., & Mahmud, S. (2009). A model for determining student plagiarism: Electronic detection and academic judgement. Journal of University Teaching & Learning Practice, 6(1). http://ro.uow.edu.au/jutlp/vol6/iss1/6
  4. Clarke, R., & Lancaster, T. (2006). Eliminating the successor to plagiarism: Identifying the usage of contract cheating sites. Paper presented at the Second International Plagiarism Conference, The Sage Gateshead, Tyne & Wear, United Kingdom.
  5. Eaton, S. E., Mindzak, M., & Morrison, R. (2021, June 1). The impact of text-generating technologies on academic integrity: AI & AI Canadian Association for the Study of Educational Administration (CASEA), University of Alberta [online]. http://hdl.handle.net/1880/113569
  6. Eaton, S. E., Stoesz, B. M., Godfrey Anderson, J. R., & LeBlanc-Haley, J. (2022, April 25). Academic integrity through an equity lens: Policy analysis of universities in Atlantic Canada American Educational Research Association (AERA) [online], San Diego, USA. http://hdl.handle.net/1880/114574
  7. Garrison, D. R., & Cleveland-Innes, M. (2005). Facilitating cognitive presence in online learning: Interaction is not enough. American Journal of Distance Education, 19(3), 133-148.
  8. Hayden, K. A., Eaton, S. E., Pethrick, H., Crossman, K., Lenart, B. A., & Penaluna, L. A. (2021). A scoping review of text-matching software used for student academic integrity in higher education. Education Research International, 2021, 1-15. https://doi.org/10.1155/2021/4834860 · DOI
  9. Lancaster, T. (2022, May 6). Artificial intelligence led threats to academic integrity [online] European Network on Academic Integrity (ENAI) 8th European Conference on Academic Integrity and Plagiarism (ECAIP) 2022, Porto, Portugal.
  10. Lang, J. M. (2013). Cheating lessons: Learning from academic dishonesty. Harvard, MA: Harvard University Press.
  11. Morris, E. J. (2016). Academic Integrity: A Teaching and Learning Approach. In T. Bretag (Ed.), Handbook of Academic Integrity (pp. 1037-1053). Singapore: Springer Singapore.
  12. Mortati, J., & Carmel, E. (2021). Can we prevent a technological arms race in university student cheating? Computer. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9548108
  13. Parnther, C., & Eaton, S. E. (2021, June 23). Academic integrity and anti-Black aspects of educational surveillance and e-proctoring. Teachers College Record. https://journals.sagepub.com/pb- assets/cmscontent/TCZ/Commentaries%20Collection/2021%20Commentaries/Academic%20Int egrity%20and%20Anti- Black%20Aspects%20of%20Educational%20Surveillance%20and%20E-Proctoring- 1650325575.pdf
  14. Stoesz, B. M., & Eaton, S. E. (2020). Academic integrity policies of publicly funded universities in western Canada. Educational Policy. https://doi.org/https://doi.org/10.1177/0895904820983032 · DOI
  15. Stoesz, B. M., Eaton, S. E., Miron, J. B., & Thacker, E. (2019). Academic integrity and contract cheating policy analysis of colleges in Ontario, Canada. International Journal for Educational Integrity, 15(4), 1-18. https://doi.org/https://doi.org/10.1007/s40979-019-0042-4 · DOI
  16. Thomas, J., & Scott, J. (2016). UK perspectives on academic integrity. In T. Bretag (Ed.), Handbook of Academic Integrity (pp. 39-53). Singapore: Springer Singapore.
  17. Vaughan, N. D., Cleveland-Innes, M., & Garrison, D. R. (Eds.). (2013). Teaching in blended learning environments. Athabasca University Press. http://www.aupress.ca/books/120229/ebook/99Z_Vaughan_et_al_2013- Teaching_in_Blended_Learning_Environments.pdf
  18. Weber-Wulff, D. (2016). Plagiarism detection software: Promises, pitfalls, and practices. In T. Bretag (Ed.), Handbook of Academic Integrity (pp. 625-638). Singapore: Springer Singapore.

Cited by

Sorted by citations

Authors
Eaton, S. E.
Affiliations
University of Calgary (CA)
Corresponding author
Eaton, Sarah Elaine
ORCID
Eaton, Sarah Elaine
Venue
Canadian Journal of Learning and Technology
Publisher
University of Alberta Libraries
Published
2023-01-21
Volume
48
Issue
2
ISSN
  • 1499-6677 (ISSN-L)
  • 1499-6685
DOI
10.21432/cjlt28388
OpenAlex
W4317706999
Cited by
19
References
2
Citations by year
2026: 4 · 2025: 7 · 2024: 7 · 2023: 1
FWCI
4.76
Type
journal article
Language
en
Open access
Licence
Locations
  • Canadian Journal of Learning and Technology (publishedVersion, cc-by-nc)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Érudit (Université de Montréal) (submittedVersion)
Topics
  • Social Sciences › Safety Research › Academic integrity and plagiarism
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Law › Legal Issues in Education
Keywords
academic integrity, academic dishonesty, text-matching software, online proctoring, large language models, artificial intelligence in education, academic misconduct prevention, student success

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