Academic Integrity and Artificial Intelligence: An Overview
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?
Students need to have meaningful, and multiple, ways to express their learning and meet the learning outcomes of a course.
“In addition, students need to have meaningful, and multiple, ways to express their learning and meet the learning outcomes of a course (Kumar et al., 2023).”
From Evolving intersections: AI, disability, and academic integrity in higher education (2025) · cites this paper as Kumar et al., 2023
Cites
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · see citing works
The Creation and Detection of Deepfakes: A Survey
Mirsky, Y., & Lee, W. (2020) · arXiv · doi:10.48550/arxiv.2004.11138 · Cited by 863
The Creation and Detection of Deepfakes: A Survey
Mirsky, Y., & Lee, W. (2022) · ACM Computing Surveys · doi:10.1145/3425780 · Cited by 863 · see citing works
Plagiarism in Higher Education: Tackling Tough Topics in Academic Integrity
Eaton, S. E. (2021) · doi:10.5040/9798400697142 · Cited by 199 · see citing works
Self-citationRogerson, A. M. (2017) · International Journal for Educational Integrity · doi:10.1007/s40979-017-0021-6 · Cited by 93 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Qorich, M., & El Ouazzani, R. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128405 · Cited by 6 · see citing works
Evolving intersections: AI, disability, and academic integrity in higher education
Jafry, A., & Vorstermans, J. (2025) · New Directions for Teaching and Learning · doi:10.1002/tl.20629 · Cited by 6 · see citing works
Intelligent Immersion: AI and VR Tools for Next-Generation Higher Education
Liakopoulos, K., & Liapakis, A. (2026) · AI in Education · doi:10.3390/aieduc2020013 · Cited by 1 · see citing works
- Authors
- Kumar, R., Eaton, S. E., Mindzak, M., & Morrison, R.
- Affiliations
- Brock University (CA)
- University of Calgary (CA)
- George Brown College (CA)
- Corresponding author
- Kumar, Rahul
- Venue
- Handbook of Academic Integrity
- Publisher
- Springer Nature Singapore
- Published
- 2023
- Pages
- 1-14
- OpenAlex
- W4382567762
- Cited by
- 35
- References
- 6
- Citations by year
- 2026: 10 · 2025: 9 · 2024: 12 · 2023: 4
- FWCI
- 43.74
- Type
- book chapter
- Language
- en
- Open access
- closed
- Licence
- Locations
- Springer eBooks (publishedVersion)
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- academic integrity, artificial intelligence in education, large language models, GPT-3, algorithmic writing, plagiarism, AI detection, academic writing instruction, institutional 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
