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Aipedia: Training providers and educators
AI-writing detectors can flag possible misuse, but their scores are not proof and should never be the sole basis for a sanction. Unfair findings lead to appeals, discrimination complaints and claims against the provider.
Part of: AI in training providers and educators
In short
Some providers may run learner work through AI-writing detectors. These tools produce a probability, not evidence, and can flag formal writing, second-language writing and work produced with permitted assistive tools. Ofqual wants stronger detection and authentication measures but has not endorsed any tool, and expects fair, valid outcomes. JCQ guidance should inform centres' malpractice policies. Sanctions based on scores alone are hard to defend and can raise discrimination allegations. Use detectors as a prompt for investigation, gather other evidence, and let the learner respond before deciding.
Faced with essays and portfolios that may have been written by a generative AI chatbot, some providers may run submissions through AI-writing detectors. These tools produce a score or label suggesting how likely it is that text was machine-generated. They are quick and they feel objective, which is exactly why they are risky when used as proof.
A detector does not show that a learner cheated. It reports a pattern in the text. Writing that is formal, formulaic, heavily edited, or produced by someone writing in a second language can look similar to AI output. Learners who used permitted tools such as spelling and grammar checkers may also be flagged.
Ofqual's July 2026 AI position says students must not present AI-generated work as their own and that awarding organisations must manage misuse and assure authenticity. Ofqual has written to exam boards requiring stronger deterrence, detection and authentication measures. It has not endorsed any particular detection tool, and it stresses that assessment decisions should be valid, reliable and fair.
JCQ's AI Use in Assessments guidance (Revision 2, 30 April 2025) is the reference for centres delivering GCSEs, A levels and similar qualifications, and should shape the centre's malpractice policy, including how suspicions are raised and investigated. Commercial trainers have no sector code, but the same fairness expectations apply to any certificate they issue.
Illustrative scenario: a professional training provider uses a detector on final assignments for a diploma that employers rely on. A learner's report scores highly and the provider fails the assignment without interview. The learner, who has dyslexia and used assistive software allowed under their adjustments, shows drafts and notes proving authorship. They complain that the sanction was unfair, delayed their qualification and was discriminatory.
The provider's problem here is not the tool, it is relying on a probability score as if it were evidence, and ignoring other information. Sanctions based on detector scores alone are hard to defend in an appeal, a complaint to an ombudsman or redress scheme where one applies, or a court claim. Discrimination arguments arise where flags fall more heavily on particular groups, such as disabled learners or those writing in a second language; see our note on the Equality Act 2010 disability provisions.
A claim that a provider negligently assessed a learner, or wrongly withheld a result causing financial loss, may fall within a trainers' PI policy, subject to its terms. Discrimination allegations are sometimes excluded or limited, and reputational harm from an accusation may need defamation cover. Check how your policy treats each of these. If a learner threatens legal action, treat it as a possible circumstance to notify; see circumstance notification explained.
Apex arranges PI for training providers and educators and can check how your policy treats assessment disputes, discrimination allegations and defamation. See the training providers Aipedia hub, learner AI malpractice, AI remote proctoring and whether trainers' PI covers AI mistakes.
It is risky to do so on the score alone. A detector gives an indication, not proof. Use it to prompt a closer look, gather other evidence, give the learner a chance to respond and follow your malpractice procedure. Decisions based solely on a score are hard to defend on appeal.
Ofqual has asked exam boards for stronger deterrence, detection and authentication measures, but it does not mandate a particular tool. JCQ's AI Use in Assessments guidance should inform centres' AI and malpractice policies. How you detect misuse is for your procedures, provided they are fair.
Detectors look for patterns typical of machine text. Plain, formal or formulaic writing, writing by people using a second language, and work produced with permitted assistive tools can share those patterns. That creates a risk of unfair outcomes and possible discrimination complaints.
Yes, it is good practice. Say in your assessment or malpractice policy that work may be checked with software, that a score alone will not lead to a sanction, and how learners can respond. Transparency makes the process fairer and easier to defend if challenged.
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