Catching the wrong student: AI detection, international students and the fairness crisis in UK universities.

This blog was written by Brendal Aformeziem, a PhD student at the University of Strathclyde.

Last summer, a student at a British university explained during his illegal activities that he had used Google to look up synonyms because English was not his first language. The panel recorded this as an admission of paraphrasing using AI. The Office of the Independent Adjudicator (OIA), the official higher education ombudsman for England and Wales, partially upheld the student’s complaint. One of the consequences is that the university has never considered whether Turnitin’s AI detection may perform less reliably for users whose first language is not English. This despite a significant amount of research showing exactly this.

In July 2025, OIA published summaries of four cases involving students accused of AI-assisted misconduct. Three involved international or second language students, and three were endorsed or partially endorsed by the university. This is not an isolated failure, but a predictable consequence of deploying unreliable tools to a student population that has never been properly served.

The biases built into these tools have been documented for some time. A study published in the journal Patterns by Stanford University researchers tested seven widely used AI detectors and found that more than 61% of essays written by non-native English speakers were misclassified as AI-generated, while the same tools showed near-perfect accuracy for native English-speaking writers. A structural explanation is that students writing in a second language tend to use shorter sentences, lower word variety, and less idiomatic expressions. These are patterns that detection algorithms associate with machine-generated text. The tool cannot tell the difference between an artificial intelligence and a student doing their best in a language that is not their own.

The broader reliability picture is not reassuring. A recent large-scale evaluation tested the detection tool across 805 samples and found an average accuracy of just 39.5% on unmodified AI-generated text. When students applied simple techniques, accuracy dropped to 17.4%, with some tools incorrectly classifying half of all human-written text as AI. Weber-Wulff et al.’s study, the most comprehensive and independent evaluation to date, found that none of the tools tested met the standards required for reliable use in high-stakes decisions. Turnitin’s Chief Product Officer acknowledged that detection scores are probabilistic, not definitive, and should not be the sole basis for misconduct proceedings. British universities often do that.

The OIA example shows how this plays out in practice. In the second case, from July 2025, the international student singled out by Turnitin was not presented with evidence against him during his lifetime. No opportunity was given to provide written mitigation evidence. Despite the student’s belief that it was an acceptable language support tool, the use of Grammarly was ignored without explanation. OIA fully acknowledged the complaint. In a third case, a student with autism was accused, punished, and ultimately granted a retrial. Previous submissions had already been flagged by the same detection software, and even then they were found to be entirely human-written. The tool identified suspicious writing styles. In both cases, no AI was used.

A study by Arslan et al. We found that international students in the UK primarily use AI tools to overcome language barriers, navigate unfamiliar academic conventions, and meet the writing standards required by their programs. They are trying to live up to the expectations of the system, not game it, and the tools universities use to detect misconduct make them structurally more likely to report it than any other group.

This is important both financially and morally. International students make up 24% of all higher education students and 51% of all postgraduate students in the UK. With 43% of UK universities projecting a deficit, international tuition revenue is not a peripheral concern. Universities depend financially on these students while deploying systems that treat them as the highest-risk population for academic dishonesty, a position that is neither sustainable nor defensible.

Leading institutions elsewhere have already reached this conclusion. The University of Waterloo discontinued Turnitin’s AI detection in September 2025, citing unreliability and risk of unfair accusations. Curtin University followed suit in January 2026, while UCLA and UC San Diego made the same request in 2024. British universities have not followed suit, but the evidence is now overwhelming that they should.

The alternative to detection is not to abandon academic integrity, but to pursue it through effective approaches. Francis et al. (2025) and Newton and Jones (2025) identify the same evidence-based alternatives, such as process-based assessment, step-by-step submission, oral defense, and AI literacy embedded in the curriculum. MIT’s Sloan School of Management concluded that AI detectors don’t work and that the right institutional response is redesign of evaluation rather than better surveillance. Although these approaches are more difficult to validate and design for learning than police outcomes, they are fairer, more pedagogically defensible, and are much less likely to place international students before a misconduct panel for the offense of writing in a second language.

Policy questions are specific. Universities should stop using AI detection as the primary evidence base for their misconduct proceedings until there is independent verification of the tools they are using. The Quality Assurance Agency (QAA) and OIA should issue joint guidance making clear that detection scores alone cannot be the basis for disciplinary action. And universities should commit to assessment redesign programs, with dedicated staff support, that reduce reliance on essay-based submissions that cannot reliably distinguish between AI use and legitimate writing support.

Three years into the generative AI era, UK universities are still dealing with academic integrity as a detection problem. An OIA case published in July 2025 illustrates the cost of choice. The question in 2026 will be whether institutions will change their courses before more students start paying.

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