A Peer Reviewer Flagged My Paper as AI. What Turnitin Says

Turnitin's FAQ is explicit about the limits of its AI writing indicator:

"Hence, we must emphasize that the percentage on the AI writing indicator should not be used as the sole basis for action or a definitive grading measure by instructors."

This is a statement from Turnitin itself. The percentage is a signal, not a conclusion. If a peer reviewer flagged your paper based on a Turnitin score, the documentation says that score was never intended to be the final word. What journals actually run on a submission is a separate question: do journals check papers for AI writing.

The FAQ frames the score as one input among many:

"It is not meant to provide definitive answers in isolation. More important than any tool is the educator who sees the score and makes decisions balancing this information with their personal knowledge of their students, their work, and institutional policy."

The score is designed to be interpreted by an educator who has context the tool does not. It does not know your writing process, your draft history, or your voice. The documentation says the person reading the score matters more than the score itself.

A peer reviewer who flags your paper based on the percentage alone is using the tool in a way the FAQ says it should not be used. The score is meant to be balanced against personal knowledge of the student and their work.

The FAQ describes specific text features that can trigger false positives:

"Sometimes false positives (incorrectly flagging human-written text as AI-generated), can include content without a lot of structural variation, text that literally repeats itself, or text that has been paraphrased without developing new ideas."

If your paper was flagged, consider whether it has any of these characteristics. Text that lacks structural variation, repeats the same phrasing, or paraphrases sources without adding new analysis can be flagged even if you wrote every word yourself. These are documented false positive patterns, not speculation.

This is worth noting because these features are common in academic writing. Literature reviews, methodology sections, and results descriptions can all exhibit low structural variation or repetitive phrasing by nature. A false positive does not mean the text is AI-generated. It means the text shares patterns the model associates with AI output. Turnitin flagged my whole methodology section is that case worked through in detail.

The FAQ states a specific false positive target:

"We strive to maximize the effectiveness of our detector while keeping our false positive rate - incorrectly identifying fully human-written text as AI-generated - under 1% for documents with over 20% of AI writing."

The next sentence: "In other words, we might flag a human-written document as AI-written for one out of every 100 fully-human written documents."

The target is under 1% for documents with over 20% AI writing. This means Turnitin has documented that false positives happen, and has set a threshold for how often they should happen. The threshold is a target, not a guarantee. False positives are part of the system's design parameters. What a 1% false positive rate means when a university submits 75,000 papers turns that target into a headcount.

The FAQ notes that access to the AI indicator is restricted:

"Please note, only instructors and administrators are able to see the indicator."

Students do not see the score. Peer reviewers in many editorial workflows may not have direct access to the Turnitin AI indicator either. If a peer reviewer flagged your paper as AI, they may have been working from the similarity report, a different detector, or their own assessment of the writing. It is worth clarifying what tool produced the flag and who had access to what report. On the journal side that usually means iThenticate: does iThenticate detect AI writing.

To summarize what the documentation says:

• The percentage on the AI writing indicator should not be used as the sole basis for action or a definitive grading measure.
• The score is not meant to provide definitive answers in isolation. The educator's knowledge of the student and institutional policy matter more.
• False positives can include content without structural variation, text that repeats itself, or text paraphrased without developing new ideas.
• The false positive target is under 1% for documents with over 20% AI writing. False positives are a documented part of the system.
• Only instructors and administrators can see the AI indicator. Students do not see it.

If a peer reviewer flagged your paper and you want to address the passages that were flagged, import the report and work on them. Eligible passages can be re-run at no charge.