In the nexus of integrity and surveillance: Proctoring (re)considered
Open accessAbstract
Background: The Covid‐19 pandemic disrupted higher education in many ways, such as the move to Emergency Remote Online Teaching and Learning (EROTL), often including a move to online assessments and examinations. With evidence of increased academic dishonesty in unproctored online assessment, institutions sought ways to ensure academic and institutional integrity and reputation. In doing this, many institutions selected and implemented online proctoring solutions. Objectives: This article maps considerations of online proctoring solutions in the nexus between ensuring academic and institutional integrity and reputation, and addressing stakeholder concerns regarding invasive surveillance and the impacts on student privacy. Methods: The study involved a PRISMA‐informed systematic review of three digital libraries, namely Clarivate's Web of Science, Elsevier's Scopus, and Springer's SpringerLink, for peer‐reviewed journal articles and conference proceedings. After screening, a final corpus of 27 articles was analysed. Results and Conclusions: The findings include evidence that, in the midst of the Covid‐19 pandemic, higher education institutions were largely influenced by cost, usability and efficiency in choosing online proctoring solutions to ensure academic and institutional integrity. Student privacy was either considered in terms of data protection and transparency, or not at all. This article aims to provide valuable insight into the criteria used to select online proctoring solutions to ensure academic and institutional integrity in online examination environments. Student privacy appears not to have the consideration it warrants.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
As online education grows increasingly popular, the issues of AI plagiarism and detecting academic misconduct with the use of artificial intelligence have become more pressing.
“As online education is growing increasingly popular, an environment where the use of chatbots is particularly easy, the issues of (a) AI plagiarism and (b) detecting academic misconduct with the use of artificial intelligence [24,43] have become more pressing [96,97].”
From New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution (2023) · cites this paper as [96]
Cites
Contract cheating by STEM students through a file sharing website: a Covid-19 pandemic perspective
Lancaster, T., & Cotarlan, C. (2021) · International Journal for Educational Integrity · doi:10.1007/s40979-021-00070-0 · Cited by 312 · see citing works
A Systematic Review on AI-based Proctoring Systems: Past, Present and Future
Nigam, A., Pasricha, R., Singh, T., & Churi, P. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10597-x · Cited by 267 · see citing works
Reedy, A., Pfitzner, D., Rook, L., & Ellis, L. (2021) · International Journal for Educational Integrity · doi:10.1007/s40979-021-00075-9 · Cited by 168 · see citing works
A necessary evil? The rise of online exam proctoring in Australian universities
Selwyn, N., O’Neill, C., Smith, G., Andrejevic, M., & Gu, X. (2023) · Media International Australia · doi:10.1177/1329878x211005862 · Cited by 117 · see citing works
Testing in the time of COVID-19: A sudden transition to unproctored online exams
Clark, T. M., Callam, C. S., Paul, N. M., Stoltzfus, M. W., & Turner, D. (2020) · Journal of Chemical Education · doi:10.1021/acs.jchemed.0c00546 · Cited by 110 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution
Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023) · Sustainability · doi:10.3390/su151612451 · Cited by 1,089 · see citing works
Will ChatGPT get you caught? Rethinking of Plagiarism Detection
Khalil, M., & Er, E. (2023) · doi:10.35542/osf.io/fnh48 · Cited by 259 · see citing works
Self-citation
- Authors
- Khalil, M., Prinsloo, P., & Slade, S.
- Affiliations
- University of Bergen (NO)
- University of South Africa (ZA)
- Earth Trust (GB)
- Corresponding author
- Khalil, Mohammad
- Venue
- Journal of Computer Assisted Learning
- Publisher
- Wiley
- Published
- 2022-07-11
- Volume
- 38
- Issue
- 6
- Pages
- 1589-1602
- ISSN
- 0266-4909 (ISSN-L)
- 1365-2729
- OpenAlex
- W4285042456
- Cited by
- 47
- References
- 6
- Citations by year
- 2026: 11 · 2025: 9 · 2024: 12 · 2023: 13 · 2022: 2
- FWCI
- 10.44
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Computer Assisted Learning (publishedVersion, cc-by)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Social Sciences › Education › Education during COVID-19 pandemic
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- online proctoring, academic integrity, institutional integrity, surveillance, student privacy, online examinations, online assessment, academic dishonesty, COVID-19 pandemic, emergency remote teaching, data protection, higher education institutions
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