(28 Aug 2026) For most of the noughties, plagiarism policies rested on three assumptions: that copying was a moral failure rather than a developmental one; that one standard of “acceptable help” could be applied fairly to students with very different resources; and that software could tell us who was guilty. It took a decade of research to establish that all three assumptions were wrong. We are now writing AI policies as if none of it happened.
Let’s think about what we got wrong the first time. We moralised a developmental problem. Patchwriting – copying and lightly modifying source text – is a stage nearly every novice writer passes through, research shows, while most textual plagiarism by second-language writers involves no intentional deception, other studies reveal. Nevertheless, we processed these students through misconduct panels rather than teaching them.
Research on proofreading shows that the limits of acceptable third-party assistance remain disputed and mostly unregulated, creating a grey area that particularly affects second-language writers. Yet we pretended our standards were neutral.
And we trusted the machines. Text-matching software returned a number and institutions treated it as a verdict. In 2020 I took part in a multi-country evaluation of those tools; they varied enormously in what they detected, and not one could responsibly stand in for human judgement. They were support tools. We used them as juries.
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