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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteSometimes, but not reliably enough to treat a detector result as proof of who wrote a passage. Turnitin says its English AI Writing Report can flag qualifying prose it classifies as AI-generated and then modified with an AI paraphrasing tool or word spinner. The company also warns that its model can misidentify human, AI-generated, and AI-paraphrased text. Independent 2023 research showed that paraphrasing sharply reduced detection in one specific experiment.
Can AI detectors identify edited or paraphrased text?
Some tools claim they can identify AI-generated text after certain kinds of rewriting, but the answer depends on the detector, its version, the language and format, and how the text was changed. Light human editing, machine paraphrasing, and translation are different conditions; evidence about one does not establish performance on the others.
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A detector produces a classification based on patterns in text. It does not reconstruct a writer’s process or prove which person—or tool—created a passage. A high score is not proof of AI use, and a low or zero score does not prove human authorship.
Can Turnitin detect AI-paraphrased text?
Turnitin’s current guidance describes two categories in its AI Writing Report: text it classifies as likely AI-generated, and text it classifies as likely AI-generated and then modified using an AI paraphrasing tool or word spinner. These labels report the model’s assessment; they are not a definitive account of how a writer produced the text. See Turnitin’s AI writing detection model guidance and its AI Writing Report guide.
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The paraphrase and bypasser capability is documented for Turnitin’s English detector. Turnitin says its Spanish and Japanese detectors do not currently include those capabilities. Product features can change, so check the live documentation when interpreting a report.
What the report percentage covers
Turnitin defines the overall percentage as the share of qualifying text in a submission that it identifies as likely AI-generated, or likely AI-generated and then altered with an AI paraphrase tool. It is not an assessment of every word or format in the document.
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For current reports, Turnitin suppresses a numeric result above 0% and below 20%, displaying an asterisk instead because low results have a higher incidence of false positives. Reports generated before July 8, 2024 may display a numeric score below 20%, so the date matters when reviewing an older screenshot. The report’s thresholds and interpretation are described in Turnitin’s AI Writing Report guide.
Language, length, and format limits
Turnitin’s guide lists English, Spanish, Japanese, and Arabic as supported report languages. It specifies at least 300 words of prose in long-form writing and allows submissions up to 30,000 words. Those eligibility requirements do not mean every kind of text is reliably assessed: Turnitin warns that poetry, scripts, code, bullet points, tables, and annotated bibliographies may not be reliably detected.
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What independent research shows about paraphrasing
A 2023 preprint by Kalpesh Krishna, Yixiao Song, Marzena Karpinska, John Wieting, and Mohit Iyyer tested DIPPER, an 11-billion-parameter paraphrase-generation model, against several AI-text detectors and text-generation models. In one specific GPT-2 XL and DetectGPT setup, paraphrasing reduced detection accuracy from 70.3% to 4.6% at a fixed 1% false-positive rate. That result applies to the paper’s experimental conditions—not to Turnitin, every detector, or current writing tools. Read the study, “Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense”.
The same paper reported that its own retrieval-based defense detected 80% to 97% of paraphrased generations across tested settings while classifying 1% of human-written sequences as AI-generated. This was a study-specific result for the authors’ method and datasets, not a performance guarantee for commercial products.
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These findings demonstrate why a detector result needs context, not that paraphrasing will reliably evade a particular current system. There is no established universal accuracy figure for all detectors applied to edited or paraphrased text.
Why a detector score is not a verdict
False positives can affect human writing
Turnitin warns that its model may misidentify human-written, AI-generated, and AI-paraphrased text, and says its report should not be the sole basis for adverse action against a student. OpenAI’s educator guidance likewise says its experience has not shown AI detectors to be reliable enough for consequential student judgments; it notes false positives on human-written passages and that small edits can evade detection. OpenAI’s statement is about its own experience and guidance, not an independent evaluation of every detector. See OpenAI’s educator guidance.
Results depend on what was tested
Detector claims are meaningful only alongside the conditions behind them. When evaluating a result or published performance claim, check:
- Transformation: Was the text lightly edited by a person, machine-paraphrased, translated, or processed by a bypassing tool?
- Language and genre: Was it English prose, another language, code, poetry, or a short or unconventional format?
- System and date: Which detector version and writing model were involved, and when was the test or report produced?
- False-positive rate: What proportion of human-written text was incorrectly flagged at the stated detection level?
- Evidence source: Is the claim from product documentation, a vendor-authored test, or independent research? These are different kinds of evidence.
Turnitin’s October 18, 2024 whitepaper describes its architecture and testing protocol, but its performance statements are vendor-authored. They should not be treated as a universal accuracy statistic. See Turnitin’s whitepaper.
How to interpret a suspected AI-writing result
For an academic or disciplinary decision, use a detector as a prompt for further review—not as the conclusion. Follow the institution’s applicable policy and consider the surrounding evidence:
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- Review drafts, notes, sources, and other records that show how the work developed.
- Ask the writer to explain their argument, sources, and writing process in a conversation.
- Where relevant, invite the writer to share AI conversations or document how AI was used. Such material can inform a review, but its presence or absence alone does not prove authorship.
- Consider whether the document’s language, genre, length, and formatting are within the detector’s stated scope.
OpenAI’s educator guidance discusses asking students to share relevant AI conversations and document their use of AI. That is a possible review practice, not proof that a particular student used a tool.
What to conclude
Some systems, including Turnitin’s English report, say they can flag certain AI-paraphrased text. Their classifications can be wrong, and experiments show that paraphrasing can reduce detection under particular test conditions. Neither outcome establishes who wrote a passage. Use detector results alongside policy, writing-process evidence, and human judgment rather than as a standalone verdict.
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