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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteYou cannot reliably prove from text alone that a passage was written by AI. AI detectors estimate whether wording resembles patterns in their training data; they can mistake human writing for AI and miss AI-assisted text. Treat a detector result as a reason to review the work—not as proof or the sole basis for a consequential decision.
Can you tell from the writing alone?
Usually, no. A fluent, generic, unusually polished, or formulaic passage may prompt questions, but none of those traits establishes who wrote it. People write in many styles, and AI-generated text can be edited. The finished prose does not reliably reveal how much help, if any, a model provided.
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There is also no universal accuracy figure for AI detectors. Results depend on the tool, the text, and the conditions under which it was evaluated. A score is an estimate, not an authorship verdict.
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You can ask, but the answer is not reliable evidence. OpenAI says ChatGPT does not know whether it generated a particular piece of text and may invent an answer. Its guidance describes such responses as “random and have no basis in fact.” Read OpenAI’s explanation.
What an AI detector can—and cannot—show
A text classifier looks for patterns associated with AI-generated writing and estimates the likelihood that text fits those patterns. It does not retrieve a record of who typed the words, and a percentage should not be read as the probability that a particular person used AI.
Why detector scores can be wrong
- False positives: Human writing can be flagged. OpenAI reported that its former classifier misidentified human text, including Shakespeare and the Declaration of Independence, and noted indications of disproportionate effects on some writers who learned English as a second language and on concise or formulaic writing. This is a warning about that tool’s limitations, not proof that every detector behaves identically. OpenAI’s educator guidance discusses these concerns.
- False negatives: AI-written or AI-assisted text may not be flagged. OpenAI said small edits could help evade its former classifier; substantial editing can also weaken some provenance signals.
- Short or unusual inputs: OpenAI’s retired classifier was unreliable on short text, performed worse outside English and on code, and was poorly calibrated outside its training data. Its results should not be generalized to every current detector. OpenAI’s 2023 announcement gives the historical limitations.
OpenAI discontinued that classifier on July 20, 2023, citing its low accuracy. In its English challenge-set evaluation, it identified 26% of AI-written text as likely AI-written and incorrectly labeled 9% of human-written text as AI-written. Those figures describe that historical tool and test set; they are not a current or general accuracy estimate. OpenAI cautioned that the classifier should not be used as a primary decision-making tool.
How to read a Turnitin AI Writing report
Turnitin describes its report as showing a percentage of qualifying submission text judged likely to have originated with a large language model, alongside highlighted passages. The percentage applies to the qualifying text in the submission; it is not a finding that a named student cheated or that every highlighted sentence was generated by AI. Turnitin advises educators to weigh the report with their own knowledge, other information, and institutional policy. As its guidance puts it, “No tool can replace the educator’s judgment combined with other data points to determine whether such a conversation is needed.” See Turnitin’s report guidance.
The Tool Desk
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If a detector raises a concern, look for evidence about how the work was produced and give the writer a fair chance to explain. A score alone is not enough to make a consequential judgment.
- Check the relevant rules. In a school or workplace, establish what assistance was allowed and what the applicable policy requires before drawing conclusions.
- Review context, not just the final text. Where appropriate and available, consider drafts, notes, version history, cited sources, and other relevant work. These can help explain the process, but none automatically proves who contributed what.
- Have a specific, neutral conversation. Ask the author to explain choices, sources, or how a section developed. In an educational setting, OpenAI suggests asking students to share specific ChatGPT conversations and to log and cite sources used with AI. OpenAI’s educator guidance offers examples.
- Use a detector only as one signal. If you consult a report, consider what tool produced it, what text it evaluated, and its stated limitations. Do not treat the score as a substitute for independent evidence or human review.
- Apply policy consistently. Give the writer a chance to respond and base any decision on the full context and the rules that apply—not on a detector score alone.
How text classifiers differ from watermarks
A classifier infers likelihood from patterns in the text. A watermark detector looks for a deliberately embedded signal in eligible model output. The two methods answer different questions and should not be treated as interchangeable.
| Evidence type | What it checks | What a positive result can indicate | Important limits |
|---|---|---|---|
| Text-pattern classifier | Whether text resembles patterns associated with AI-generated examples | A statistical estimate from that detector | May flag human writing or miss AI text; performance varies by tool, input, language, and evaluation conditions. |
| Embedded watermark | A signal deliberately introduced into eligible model output | For OpenAI’s supported signal, evidence that a supported OpenAI model likely generated or processed some content | Does not identify a person or establish the extent of model contribution, accuracy, ownership, or responsibility. Coverage is limited, and editing can weaken or remove the signal. |
OpenAI describes its text watermarking approach as “textGrain”: token choices are adjusted to create a pattern that a detector can test for. A detected signal has a narrower meaning than “AI wrote this.” It does not establish who used the model or how much they contributed. No detected signal does not prove that text was written by a human. OpenAI explains the scope and limits of its provenance signals.
Rank #4
Watermark coverage and detection are conditional
OpenAI’s current help page says text watermarking is EU-only for ChatGPT text, while API customers globally can opt in for text outputs from eligible settings. Coverage varies by product, model, region, export route, and creation date; not every output carries a detectable signal.
In an October 5, 2026 announcement, OpenAI said API customers globally could opt in for select models and that invisible watermarking would be added to eligible ChatGPT and Codex outputs in the EU over the following weeks. At the time of that announcement, access to the text detector was being opened to approved researchers and expert organizations. These are rollout details, not a guarantee that any particular passage can be checked. Read OpenAI’s announcement.
OpenAI’s evaluation reported detection of about 80% for 200-token passages and about 95% for 400-token passages in content such as psychology at a 1% target false-positive rate; detection was substantially lower for mathematics. In 400-token passages, replacing 10% of words with synonyms reduced detection from about 92% to 66%, and replacing 25% reduced it to 17%. These are results from OpenAI’s stated evaluation conditions, not general performance estimates for other tools or texts.
Quick Recap
What to conclude from a detector result
- A positive classifier result is a prompt to examine the evidence, not proof of AI authorship.
- A negative result does not establish that a person wrote the text without AI assistance.
- A watermark signal, when the text and product are covered, may indicate processing by a supported model; it cannot identify the user or settle responsibility.
- For a high-stakes decision, use independent context, relevant policy, and a fair human review.
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