How to Fact-Check AI-Generated Writing

Language models produce fluent, confident text. Fluency and accuracy are unrelated properties, and the gap between them is where people get into trouble.

The failure mode has a name — confabulation — and its defining feature is that the invented material looks exactly like the real material. There is no tell in the writing itself.

What gets invented most

Citations

The most common and most damaging. A model will produce a reference with a plausible author, a real-sounding journal, a sensible year and a well-formed page range — for a paper that does not exist. Sometimes it attributes a real paper to the wrong author, or cites a real author for a claim they never made.

Statistics

"Roughly 40% of…" appears constantly. The figure may be approximately right, wildly wrong, or entirely fabricated, and there is no way to tell from the sentence.

Quotations

Models generate quotations in the style of a person rather than reproducing what they said. Well-known figures attract this especially — the quotation sounds exactly like something they would say, which is precisely the problem.

Specific details

Dates, institutional affiliations, study sample sizes, legislation names, case outcomes. Anything specific enough to look authoritative.

A checking procedure

1. Mark every checkable claim. Go through the draft and highlight each name, date, number, quotation and citation. Seeing them all marked is usually sobering.
2. Search for each citation by title, in quotation marks. If a paper exists, the exact title returns results. Zero results is a very strong signal.
3. Verify through a database, not the open web. Google Scholar, PubMed, JSTOR, your library. A DOI that resolves is good evidence; an unresolvable DOI is decisive.
4. Trace statistics to a primary source. Not a blog citing a news story citing a report. The report.
5. Check quotations against a documented source. A quotation appearing only on quote-aggregation sites is unverified.
6. Read the source, not just the title. A real paper that says something different from what your draft claims is still a misrepresentation.

Signals worth a closer look

None of these prove fabrication, but each raises the odds:

• A suspiciously convenient citation that supports your argument precisely.
• Round numbers — exactly 40%, exactly 2,000 participants.
• A quotation that is unusually quotable and neatly encapsulates the point.
• Several sources by the same author appearing in a row.
• A very recent publication date. Models are least reliable on material near or past their training cutoff.
• A claim you cannot restate in your own words, because you do not actually know what it means.

Why it matters beyond the grade

For students: a fabricated citation is usually treated as academic misconduct, and "the AI produced it" is not a defence — submitting it is your act.

For anyone publishing professionally: one invented statistic that gets found undermines everything else you have written. Reputational damage is asymmetric and durable.

Working so this is manageable

Rather than generating a draft and checking it afterwards, restructure the process:

• Use AI for structure, phrasing and argument — not for facts.
• Gather sources yourself, from real databases, before drafting.
• If you ask the model about a topic, treat what comes back as leads to investigate.
• Never let a citation reach your draft without you having opened it.

That last rule is the whole discipline in one line, and it is enough on its own.

The originality report in AI Essay Writer Assistant flags text that reads as machine-written, which is a useful editing signal — but it is not a fact-checker and does not tell you whether a claim is true. That part is manual, and there is no substitute for it.