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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThere is no dependable text-only test that proves a passage was or was not written with AI. The fairest way to assess possible AI involvement is to check the applicable rules, review relevant evidence of how the text was produced, and give the writer a neutral chance to explain their work. A detector score or watermark can be one clue, but neither establishes who wrote the text or how much human judgment it contains.
Start by defining what you need to find out
“Was AI used?” can mean several different things. You might need to determine whether a writer used AI at all, whether a particular use had to be disclosed, or whether a claim or source in the text is wrong. Those questions require different evidence. A detector score cannot decide what a school, workplace, publisher, or other policy requires, nor who is responsible for a violation.
Read the relevant policy before investigating. Focus on the rule that applies to this assignment or publication, rather than treating AI use as automatically permitted or prohibited.
Review the writing process, not just the finished passage
When it is appropriate and consistent with privacy rules, look for evidence that shows how the work developed. Depending on the context, that might include drafts, version history, outlines, notes, source lists, or intermediate work. No single item proves authorship, but a set of materials may provide context that the final text cannot.
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Ask the writer to explain their research, choices, and revisions in a neutral conversation. For educational work, OpenAI recommends asking students to document and cite sources used with AI. Relevant AI conversations may also help an educator observe critical thinking and problem-solving, but this is contextual evidence—not a universal requirement or a standalone authorship test. Apply the same expectations consistently.
Compare writing fairly and avoid style-based accusations
If you have prior work to compare, choose examples that are genuinely comparable: similar genre, assignment, language, time constraints, and editing support. A difference in style may justify a question, but it does not establish AI authorship.
Polished, concise, predictable, or formulaic writing is not proof of AI use. OpenAI has warned that its retired classifier could disproportionately affect English learners and people whose writing was especially formulaic or concise; it also flagged human-written historical texts. Treat a stylistic impression as a reason to ask for context, not as a finding.
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Understand what a detector report actually says
A detector estimates whether text resembles patterns its model has learned. It does not identify an author. Turnitin describes its AI Writing Report as identifying text that “might be prepared by a generative AI tool.” Its report percentage concerns qualifying prose sentences that its model estimates could be AI-generated, or generated and then modified with an AI paraphraser or bypasser. That figure is separate from the similarity score; it is not a probability that a particular person cheated or broke a rule. Turnitin explains the limits of its AI writing detection, and its percentage guidance describes what the figure represents.
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Fit matters. Turnitin’s current guide lists a minimum of 300 words of prose, a maximum of 30,000 words and 100 MB, supported languages of English, Spanish, Japanese, and Arabic, and accepted file types of DOCX, PDF, TXT, and RTF. It says detection is not reliable for code, poetry, scripts, or short-form and unconventional writing such as bullet points, tables, and annotated bibliographies. Requirements and model behavior can change, so check the current product documentation before interpreting a report. Turnitin’s eligibility and file requirements are listed here.
Turnitin says its testing found a higher incidence of false positives in the 0–19% range. It suppresses scores and highlights above zero and below 20%, showing an asterisk instead; reports created before July 8, 2024 may display older numerical results below 20%. This display policy is intended to reduce misinterpretation. It does not make every score above that range correct. See Turnitin’s current explanation of score handling.
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Turnitin cautions: “Our AI writing detection model may not always be accurate (it may misidentify human-written, AI-generated, and AI-paraphrased text), so it should not be used as the sole basis for adverse actions against a student.” Its guidance also says that an AI report should be treated as one data point, not a definitive response. Read Turnitin’s guidance on reviewing an AI Writing Report.
Treat watermarks as narrow provenance evidence
A verified watermark can indicate that a participating system generated or processed some text, but it cannot identify the user, measure the human contribution, determine ownership or responsibility, or verify accuracy. An absent watermark does not show that a person wrote the text: the text may be edited, translated, too short, unsupported, older than the watermark system, or produced by another provider.
OpenAI’s October 2026 announcement describes textGrain, an invisible statistical signal in word choice. At announcement, API customers globally could opt in for select models while it remained off by default; OpenAI said eligible ChatGPT and Codex output in the EU would receive watermarks, with detector access initially limited to approved researchers and expert organizations. These rollout and access conditions may change. OpenAI’s textGrain announcement reports company evaluations—not a universal authorship test. At a target false-positive rate of 1%, its detector identified watermarks in about 80% of 200-token passages and about 95% of 400-token passages for content such as psychology; rates were substantially lower for mathematics. In 400-token passages, replacing 10% of words with synonyms reduced detection from about 92% to 66%, while replacing 25% reduced it to 17%. Those figures describe OpenAI’s stated test conditions and should not be generalized to arbitrary text.
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Do not ask ChatGPT to verify authorship
ChatGPT cannot reliably say whether it wrote a particular passage. OpenAI says the model has no knowledge of whether it generated specific text and may make up an answer. A self-report from ChatGPT is therefore not evidence of authorship. OpenAI explains why ChatGPT cannot reliably identify its own writing.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why one accuracy figure cannot settle the question
Detection performance varies by system, language, text length, genre, editing, and the generator involved. In 2023, OpenAI reported that its now-retired classifier identified 26% of AI-written text as “likely AI-written” on an English challenge set and incorrectly labeled human-written text as AI-written 9% of the time. OpenAI said it was unreliable on short text, performed worse in languages other than English, and could be vulnerable to edits; it retired the classifier on July 20, 2023 because of low accuracy. These historical results describe that classifier and evaluation only—not today’s detectors generally. OpenAI’s 2023 classifier announcement provides the evaluation context.
NIST’s 2025 report found substantial variation across evaluated systems: some tested generators could deceive most discriminators, while some discriminators could detect content from almost all tested generators. That is evidence of an evolving evaluation problem, not a permanent accuracy rate for a tool you may encounter. NIST’s report on AI-generated text detection discusses the variation.
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Use a consistent, documented decision process
- Identify the rule. Record what policy applies and what question it asks you to resolve.
- Gather relevant context. Review available process materials and, if appropriate, ask the writer about sources, decisions, and revisions.
- Check any detector’s fit. Confirm that the text meets the product’s language, length, format, and genre requirements. Preserve the report and its context rather than relying on a score in isolation.
- Separate evidence from interpretation. Note what a report or watermark actually indicates, and what it cannot establish.
- Give the writer a chance to respond. Apply the governing policy consistently, consider legitimate explanations, and state uncertainty where the evidence does not support a firm conclusion.
For a high-consequence decision, keep three questions distinct: what evidence exists about the process, what the applicable policy requires, and what conclusion the evidence can actually sustain. A score alone is not a sound basis for adverse action.
What you can responsibly conclude
There is no universal method that identifies the author of arbitrary text, and the available evidence here does not establish a current independent ranking of consumer detectors or a general accuracy rate across tools, languages, genres, models, and edited text. You can assess possible AI involvement more fairly by combining relevant process evidence, a neutral conversation, any properly interpreted detector or provenance signal, and the policy that governs the work. If those sources do not support a definitive conclusion, say so.
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