The Inherent Uncertainties of AI-Text Detection and the Implications for Education Institutions: An Overview
Abstract
This chapter focuses on the implications of the improving generative-AI ‘chatbot' technologies and the inevitable unreliability of attendant AI-text detection technologies. The goal of generative-AI programmers is to design AIs which produce text indistinguishable from typical human-written text: an eventuality that will render AI-text detectors redundant. The authors outline the underpinning mathematics of AI-generated and human-written text as the basis of AI-text detection, and how this leads to inherent inaccuracies and uncertainties in AI-text detection. The chapter proceeds to overview on how institutions will have to work with both the growth in use of AI and the unreliability of AI-text detection: institutions cannot avoid AI and cannot rely on 'tech' to police it. Students need to be taught how to use AIs ethically with integrity and insight and sanctioned when they do not. At the same time, institutions need to resource people to investigate students suspected of false authorship, whether commissioning a human ghost-writer or using an AI inappropriately.
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 subjected to AI-based misconduct investigations report experiencing hostility, anxiety, fear, and defeat, alongside a lasting erosion of trust in both the accusing instructor and the institution.
“Students subjected to AI-based misconduct investigations report experiencing hostility, anxiety, fear, and defeat, alongside a lasting erosion of trust in both the accusing instructor and the institution”
From ARE AI DETECTORS FAIR TO EFL LEARNERS? A LIBRARY RESEARCH ON DETECTION BIAS AND ACADEMIC INTEGRITY (2026) · cites this paper as Crockett & Howe, 2024
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
Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025 · see citing works
Artificial intelligence in higher education: the state of the field
Crompton, H., & Burke, D. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00392-8 · Cited by 2,010 · see citing works
Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023) · The International Journal of Management Education · doi:10.1016/j.ijme.2023.100790 · Cited by 1,261 · see citing works
Testing of detection tools for AI-generated text
Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., Popoola, O., Šigut, P., & Waddington, L. (2023) · International Journal for Educational Integrity · doi:10.1007/s40979-023-00146-z · Cited by 582 · see citing works
Casal, J. E., & Kessler, M. (2023) · Research Methods in Applied Linguistics · doi:10.1016/j.rmal.2023.100068 · Cited by 261 · see citing works
Ibrahim, H., Liu, F., Asim, R., Battu, B., Benabderrahmane, S., Alhafni, B., Adnan, W., Alhanai, T., AlShebli, B., Baghdadi, R., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-38964-3 · Cited by 154 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
ARE AI DETECTORS FAIR TO EFL LEARNERS? A LIBRARY RESEARCH ON DETECTION BIAS AND ACADEMIC INTEGRITY
Patty, J., & Matatula, M. Y. (2026) · J-Shelves of Indragiri (JSI) · doi:10.61672/jsi.v8i1.3557
- Authors
- Crockett, R., & Howe, R.
- Affiliations
- University of Northampton (GB)
- Venue
- Advances in Educational Marketing, Administration, and Leadership
- Publisher
- IGI Global
- Published
- 2024-02-13
- Pages
- 175-198
- ISSN
- 2326-9022 (ISSN-L)
- 2326-9030
- OpenAlex
- W4391773573
- Cited by
- 4
- References
- 7
- Citations by year
- 2026: 2 · 2024: 2
- FWCI
- 5.66
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Advances in educational marketing, administration, and leadership book series (publishedVersion)
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Keywords
- AI-generated text detection, generative AI, chatbots, AI-generated text, human-written text, detection accuracy, academic integrity, student sanctions, AI ethics education, ghostwriting, educational 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
