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Cited by 25Academic integrity and plagiarismArtificial Intelligence in EducationEthics and Social Impacts of AI

Heads we win, tails you lose: AI detectors in education

Open access
Bassett, Mark Andrew, Bradshaw, Wayne, Bornsztejn, Hannah, Hogg, Alyce, Murdoch, Kane, Pearce, Bridget, Webber, Colin·2026·Journal of Higher Education Policy and Management
DOI 10.1080/1360080x.2026.2622146
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Abstract

The increasing use of generative artificial intelligence (AI) in student assessment has led to institutional reliance on detection tools. Unlike plagiarism detection, AI detection relies on unverifiable probabilistic estimates. In this paper, we argue that generative AI detection should not be used in education due to its methodological imperfections, violation of procedural fairness, and unverifiable outputs. Generative AI detectors cannot be tested in real-world conditions where the true origin of a text is unknown. Attempts to validate results through linguistic markers, multiple tools, or comparisons with past work introduce confirmation bias rather than independent verification. Moreover, categorising text as human- or AI-generated imposes a false dichotomy that ignores work created with, not by, AI. Generative AI detection also raises security concerns. Academic integrity investigations must rely on evidence meeting the balance of probabilities standard, which generative AI detection scores do not satisfy.

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  • High-performing students, those writing with clarity and syntactical simplicity, are more likely to be flagged because their writing aligns with linguistic patterns detectors associate with AI.

    “Furthermore, Bassett et al. (2026) illustrate a paradoxical effect: high-performing students, those writing with clarity and syntactical simplicity, are more likely to be flagged because their writing aligns with linguistic patterns detectors associate with AI.”

    From Harms and Ethical Risks of AI Detection Tools in Education: False Positives, Bias, Surveillance, and Student Rights (2026) · cites this paper as Bassett et al. (2026)

    The “Mutually exclusive linguistic markers” section says that some students’ own writing will match patterns detectors associate with AI. The “Linguistic markers” section also describes staff treating polished grammar as corroboration after a detector flags work. Neither section establishes that high-performing students or those writing with syntactical simplicity are more likely to be flagged. (LLM, conf=0.86)

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Research works printed but not linked: 6

  1. Burger, C., Talley, K., & Trotter, C. (2025). Can AI recognize its own reflection? Self-detection performance of LLMs in computing education. arXiv. https://arxiv.org/abs/2512.23587 · arXiv
  2. He, J., Rungta, M., Koleczek, D., Sekhon, A., Wang, F. X., & Hasan, S. (2024). Does prompt formatting have any impact on LLM performance? arXiv. https://arxiv.org/html/2411. 10541v1/
  3. Kehkashan, T., Riaz, R. A., Al-Shamayleh, A. S., Akhunzada, A., Ali, N., Hamza, M., & Akbar, F. (2025). AI-generated text detection: A comprehensive review of methods, datasets, and applications. Computer Science Review, 55, 100926. https://doi.org/10.1016/j.cosrev.2025. 100926 · DOI
  4. Macmillan, N.A., & Creelman, C. D. (2005). Detection theory: A user's guide (2nd ed.). Psychology Press.
  5. Perkins, M., Roe, J., Vu, B. H., Postma, D., Hickerson, D., McGaughran, J., & Khuat, H. Q. (2024). GenAI detection tools, adversarial techniques and implications for inclusivity in higher education [Preprint]. arXiv. https://arxiv.org/abs/2403.19148 · arXiv
  6. Wickens, T.D. (2002). Elementary signal detection theory. Oxford University Press.

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Authors
Bassett, M. A., Bradshaw, W., Bornsztejn, H., Hogg, A., Murdoch, K., Pearce, B., & Webber, C.
Affiliations
  • Charles Sturt University (AU)
  • James Cook University (AU)
  • RMIT University (AU)
  • Macquarie University (AU)
  • Brisbane Grammar School
  • Évolution Paris Seine (FR)
Corresponding author
Bassett, Mark Andrew
ORCID
Venue
Journal of Higher Education Policy and Management
Publisher
Informa UK Limited
Published
2026-01-29
Pages
1-16
ISSN
  • 1360-080X (ISSN-L)
  • 1469-9508
DOI
10.1080/1360080x.2026.2622146
OpenAlex
W7126019496
Cited by
24
References
10
Citations by year
2026: 24
FWCI
136.17
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Higher Education Policy and Management (publishedVersion, cc-by-nc-nd)
  • ResearchOnline - JCU (James Cook University) (submittedVersion)
  • Charles Sturt University Research Output (CRO) (submittedVersion, cc-by-nc-nd)
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
generative AI detection, academic integrity, procedural justice, confirmation bias, plagiarism detection, AI security risks

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