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AI detectors can flag writing you produced yourself because they estimate how closely a passage resembles patterns associated with AI-generated text; they do not observe who typed it or how it was made. A flag is a reason to review the text and its context, not proof of authorship or misconduct. If your work is flagged, preserve your drafts and version history, check the rules that applied, ask to see the report and disputed passages, and respond with relevant evidence of your writing process.
Why can a detector flag text a person wrote?
Detectors assess statistical or structural features associated with generated language. The Tertiary Education Quality and Standards Agency (TEQSA) says commonly examined features include perplexity, burstiness, and sentence structure. Human writing can share those patterns; AI-generated text can also be edited or combined with human writing. That overlap makes it difficult to infer how a finished passage was produced.
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Concise, formulaic, or highly regular prose may resemble the patterns a detector associates with AI. So may writing by people whose first language is not English, according to OpenAI’s guidance on its early detector. OpenAI reported that its own classifier sometimes confidently labeled human-written text as AI-written; its educator guidance also says the classifier mislabeled passages from Shakespeare and the Declaration of Independence. Those are reported limitations of OpenAI’s early tool, not proof that every current product behaves the same way.
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Performance can vary with a passage’s length, language, genre, editing, and the detector’s model version. OpenAI described its 2023 classifier as especially unreliable below 1,000 characters and weaker outside English. Turnitin’s current product guidance describes a different system: its AI Writing Report requires at least 300 words of qualifying prose, and its English detector has paraphrasing and bypasser capabilities that its Spanish and Japanese detectors do not currently share. These are product-specific requirements, not universal rules for AI detectors, and product documentation can change.
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What does an AI detector score actually tell you?
A score estimates how text fits a system’s detection criteria. It is not a verified record of who wrote the text, and it is not automatically the probability that the writer used AI. Turnitin distinguishes its AI percentage from its similarity score: the two measure different things. A similarity score concerns matching text; an AI score concerns the detector’s classification of qualifying prose.
Scores also need to be interpreted in context. A false-positive rate is not the same as the chance that a particular flagged passage was written by AI. The latter depends, among other things, on how common AI use is in the group being assessed. TEQSA explains that even if no students in a group used AI, a detector could still assign high scores to some human-written submissions. A percentage alone cannot settle an individual case.
What published accuracy figures can—and cannot—show
| Evidence | Finding | How to interpret it |
|---|---|---|
| OpenAI classifier, 2023 | In an English challenge set, OpenAI said its classifier marked 26% of AI-written text as “likely AI-written” and incorrectly labeled 9% of human-written text as AI-written. | These figures describe that classifier and test set, not every detector available now. |
| Weber-Wulff et al., 2023 | The study tested 14 systems: 12 public tools and two commercial systems, including Turnitin and PlagiarismCheck. The authors concluded that the detectors tested were not accurate or reliable overall, and that obfuscation reduced performance. | The study is evidence of the difficulty of detection, not a current ranking of products whose systems may have changed. |
There is no single defensible accuracy percentage for all detectors and situations in the sources cited here. Results depend on the specific tool and sample, what counts as a positive, text length and language, and the prevalence of AI use in the setting. OpenAI’s classifier announcement said its detector should not be used as a primary decision-making tool, but as a complement to other methods of determining a text’s source.
What to do if your writing is flagged
- Preserve your process evidence. Keep the original files, notes, outline, source records, drafts, and available version history. Avoid changing or deleting material that could show how the work developed. TEQSA identifies verifiable version history as one possible form of process evidence.
- Read the applicable policy. Check the rules that applied to the assignment or publication, including what AI assistance was permitted and whether disclosure was required. Policies vary by institution and context; Turnitin’s guidance also directs instructors to start with their institution’s policy.
- Ask for specifics. Request the actual report, the passages that raised concern, the policy or rule at issue, and the process for responding. A score without the highlighted text and relevant rules gives you little basis to understand or answer the concern.
- Explain how you worked. Give a factual account and provide relevant drafts, notes, source history, or version history. Explain what each item shows; no single artifact should be presented as guaranteed proof of authorship.
- Follow formal review procedures if needed. If the matter becomes a formal misconduct case, check your institution’s appeal or review process and its deadlines. There is no universal appeal procedure established by the sources cited here.
For a formal response, keep the tone factual and organized: identify the passages in question, explain the relevant stages of your process, attach the evidence you have, and ask what further information the reviewer needs. Do not alter evidence to make it look more persuasive.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How educators and reviewers should handle a flag
- Do not decide from a percentage alone. Turnitin says its detector should not be the sole basis for adverse action against a student. TEQSA likewise says an AI score alone is insufficient to bring a misconduct allegation.
- Check what the report covers. Review the highlighted passages and confirm that the submission meets the product’s eligibility requirements, including supported language and minimum length. Turnitin warns that false positives are more likely in low score ranges; below its reporting threshold, it uses an asterisk rather than a numeric score. These details apply to Turnitin, not to detectors generally.
- Consider evidence on both sides. Look for information that could challenge as well as support the detector’s interpretation, and give the writer a chance to explain their process.
- Apply the relevant policy yourself. Turnitin says its software does not determine misconduct; the instructor or reviewer makes that decision under applicable academic and institutional policies.
How to keep a useful record of your writing process
Saving work as it develops can make it easier to explain your process later. TEQSA gives Google Docs, Microsoft 365, and Overleaf as examples of tools that may retain verifiable version history. It also names Cadmus, Inktrail, Turnitin Clarity, and Grammarly Authorship as examples of platforms with process-tracking functions. These are optional workflow tools, not proof of authorship and not a way to make a detector more accurate. Follow your institution’s privacy and tool-use rules before uploading or storing student work on any platform.
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