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Start by deciding what you need to know
Separate three questions that are easy to conflate: Is the text accurate? Does it follow the rules that apply to this work? And is there evidence that AI helped produce it? A text can be generic, incorrect, or poorly sourced whether it was written by a person or a model. Fact-checking can establish whether claims hold up; it does not, by itself, identify the author.
If the concern is a possible policy violation, check the relevant organization’s rules and process before investigating. The appropriate standard and consequences depend on the institution, jurisdiction, and context.
Use an evidence-first review
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Pull out checkable claims
Mark dates, figures, quotations, technical or legal claims, and statements about named people or organizations. Prioritize claims that matter to the text’s conclusion or could cause harm if wrong.
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Verify the sources behind them
Open each important citation. Confirm that the source exists, is authoritative for the subject, is current enough, and actually supports the sentence citing it. Prefer primary or otherwise authoritative sources, then look for independent confirmation when the claim is consequential. The House of Commons Library recommends this kind of claim-by-claim checking in its guidance on working with AI and spotting AI-generated text.
A citation can be real yet irrelevant, outdated, or weaker than the claim suggests. Conversely, an unsourced or incorrect statement is a reason to correct or investigate the text, not evidence on its own that AI wrote it. The Commons Library notes that AI systems can make errors, hallucinate, and misrepresent sources; treat factual output as unverified until checked.
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Note anomalies, but do not treat them as a verdict
Possible warning signs include fluent but vague prose, unsupported assertions, citations that do not quite match their claims, repeated ideas, errors in names or dates, or an abrupt change in tone or level of detail. The Commons Library explicitly says, “These indicators are not definitive.” People can write this way, and AI-generated text can be edited to hide such patterns.
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Compare with prior writing only when the comparison is fair
If you have a legitimate, credible sample by the same writer, compare specific features such as terminology, reasoning, source use, and treatment of the subject—not just whether the prose sounds different. Account for changes in topic, audience, editing, language proficiency, templates, translation, tutoring, and permitted assistance. Indiana University’s guidance says a change from a student’s usual style may begin a conversation, but is not proof of AI use.
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Ask focused, non-accusatory questions
Ask the writer to explain a key claim, why a source supports it, how they developed an argument, or what they changed during revision. A useful conversation tests understanding and invites context; it is not a trap or a substitute for evidence.
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Review process records only when permitted
Where appropriate under the applicable rules, a document’s version history or a platform’s writing record may provide context about how work developed. TEQSA, Australia’s higher-education regulator, discusses process records as support for assessment security and feedback—not as an automatic authorship verdict. Confirm that you have authority to inspect the records and interpret them in context.
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Look for evidence on both sides
Consider explanations that could account for a change or anomaly, including editing, tutoring, translation, a template, or different assignment conditions. TEQSA recommends considering evidence that could disconfirm AI use as well as evidence that might support it. Record what is known, what remains uncertain, and what would change your assessment.
What each kind of evidence can—and cannot—show
| Evidence | What it can help establish | What it cannot establish alone |
|---|---|---|
| Fact-checking claims and citations | Whether statements are accurate and references substantiate them. | Who wrote the text or whether AI was used. |
| Style or tone differences | Whether a passage differs from a fair comparison sample enough to merit clarification. | AI authorship; style varies for many reasons and can be edited. |
| Questions about reasoning and sources | Whether the writer can explain the work and its choices. | A definitive record of how every sentence was produced. |
| Version history or other process records | Context about how a document developed, if records are genuine and access is permitted. | An automatic finding of misconduct or a complete account of authorship. |
| A chatbot’s answer about whether it wrote the text | Nothing reliable about authorship. | Verification. OpenAI says ChatGPT has no knowledge of whether it generated a particular passage and may invent an answer to that question. |
OpenAI’s Help Center puts the limitation plainly: “ChatGPT has no ‘knowledge’ of what content could be AI-generated or what it generated.” It also warns that answers to questions such as whether ChatGPT wrote an essay may be made up and have no basis in fact. Do not use a chatbot’s self-report as corroboration.
Why style clues and detector scores are not proof
There is no reliable style-only test that establishes AI authorship. NIST’s 2024 GenAI text-to-text pilot study, published June 25, 2025, found that performance varied across the systems it evaluated: some generators could deceive most discriminators, while some discriminators detected content from almost all generators. That is a study-specific result, not a guarantee about every current text or tool.
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For academic cases, TEQSA warns that false positives occur and that detectors are less reliable on edited or mixed text and short documents. It states that “The ‘AI score’ alone is insufficient to bring an allegation of misconduct.” A detector score is not the probability that an assignment was AI-generated. If a detector has already been used, treat its output as a lead at most and follow the institution’s evidence and appeal procedures.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Protect private or submitted work
Before uploading a nonpublic document to any third-party service, check the applicable privacy, intellectual-property, and organizational rules. Indiana University’s cited guidance warns instructors not to upload student work to detection tools under its university guidelines, citing privacy and intellectual-property concerns. That is a university-specific instruction, not a universal policy; follow the rules that apply to your role and institution.
Handle formal allegations and disclosures carefully
In Australian higher education, TEQSA describes substantiating misconduct as requiring proof on the balance of probabilities and recommends assessment redesign and secure assessment for key learning outcomes. This is context-specific guidance; do not assume the same standard applies at every school, employer, or jurisdiction. Use the relevant institution’s procedure and give the writer the process and opportunity to respond that its rules require.
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For scientific writing, CDC guidance recommends disclosing substantive generative-AI use. A disclosure should identify the affected content, what action was taken, the AI tool and model or version where available, the purpose, and human oversight. Publishers, funders, employers, and collaborators may set additional requirements, so check their current policies.
How to report a cautious conclusion
Match the conclusion to the evidence. If you have verified errors but no authorship evidence, report the errors. If a style change or citation problem warrants follow-up, say that the work deserves clarification. Reserve a formal finding for a process that meets the applicable policy and is supported by corroboration—not an impression about how the prose sounds.
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