How to read a Turnitin AI writing report

This is the most misread number in the report. Turnitin defines it as the share of qualifying text, and qualifying text means prose sentences in a long-form writing format: sentences inside paragraphs, in something shaped like an essay, a dissertation, an article.

Everything else is excluded from the calculation. Turnitin states the model does not reliably detect AI writing in non-prose, naming poetry, scripts and code, along with short-form or unconventional writing: bullet points, tables, annotated bibliographies.

A 30 percent score on a paper that is half tables and lists is not "30 percent of your paper". It is 30 percent of the prose Turnitin was willing to assess, which may be a third of what you submitted.

There is also a floor. A submission needs at least 300 words of prose in long-form format before a report is generated at all. Below that there is no percentage - not a low one, none. There is a ceiling too: the AI report caps at 30,000 words, so a full-length thesis does not get one report covering all of it.

Readers routinely notice that the highlighted passages do not look like they add up to the stated percentage and conclude something is broken. Nothing is broken. Turnitin says it directly: a document containing several different writing types will produce a disparity between the percentage and the highlights.

The percentage is computed over qualifying text. The highlights sit in a document that also contains non-qualifying text. The denominators differ, so the two will not reconcile by eye, and checking one against the other is wasted effort.

What the highlights are good for is distribution. Where a flag falls tells you more than how much of it there is.

The report splits its percentage into two kinds of detection.

• AI-generated only - text the model judges likely to have come from a large language model, possibly then modified by a bypasser.
• AI-generated text that was AI-paraphrased - text judged likely AI-generated and then likely run through an AI paraphrasing tool or word spinner. Turnitin names QuillBot as an example.

The second category is worth sitting with. The paraphraser step is not invisible to the model: running AI text through a rewriter is a pattern the detector was built to recognise as such, rather than a way around it. If you have wondered why paraphrasing did not lower a score, this is the documented reason.

It also explains a common disappointment. A synonym swapper leaves the sentence skeleton intact, and that skeleton is what the model reads - a point worth reading alongside what detectors actually measure.

Since 8 July 2024, when the model detects AI below 20 percent, the report shows an asterisk and attributes no percentage and no highlights.

Turnitin’s stated reason is that testing found a higher incidence of false positives in the 0-19 range. Rather than publish a number it does not trust, it publishes an asterisk.

That is a vendor declining to score its own product’s low range - worth knowing before treating a small number as meaningful. If your report predates July 2024 you may still see one, generated under the old rule.

Turnitin states that it does not determine misconduct; it provides data for educators to make an informed decision under their own institutional policies. The number is an input to a human judgement, and the company says so.

That is the most useful thing to know if you are on the receiving end of one. The question is not "how do I get this number down" but "what is this number being used for, and what does my institution’s policy say it means".

If you want to see exactly which passages were flagged rather than infer it, importing the report alongside your document locates them and leaves everything else untouched.