Upload a PDF, Word (DOCX) or TXT file and get a score for the whole document — plus a page-by-page map on PDFs — built for students checking a paper before they submit it, and teachers checking whole assignments at once.
Free: first 1,500 words scored, up to 5 pages mapped. The document is sent securely over HTTPS to the TextSight API and processed on our servers.
Files aren't saved. The document is sent over HTTPS, scored, and not stored.
Upload a DOCX or PDF, or try the example, to get per-page AI scores.
See which pages read as AI-generated, not just an overall number — ideal for long assignments.
Each page goes through the detector on its own, so a spike on page 7 doesn't get averaged away.
Without an account the score reads the first 1,500 words; a free account reads up to 25,000. The page map covers the first 5 pages.
Most AI checkers expect you to paste in a block of text. That works for a single answer, but it falls apart the moment you're holding a 12-page essay, a 40-page report or a chapter draft. You end up copying sections one at a time, losing track of which slice came from where, and missing the part that actually matters: where in the document the writing shifts from a person's voice to a model's.
The Document AI Detector takes the whole file at once. It extracts the text from your PDF, keeps the page boundaries intact, and scores each page on its own — so instead of one flat number for the entire submission, you get a map. A clean opening followed by three suspiciously uniform middle pages tells you far more than a single average ever could, and it points you straight to the passages worth a closer human read. Without an account the document score is read from the first 1,500 words, and the result says so rather than presenting a partial read as a verdict on the whole file.
When you drop in a file, it's sent securely over HTTPS to the TextSight API. The first job is extraction: pulling the readable text out of the document while preserving where each page starts and ends. PDFs with a real text layer come through cleanly, page boundaries and all. DOCX and TXT files are read as one continuous document — neither format has fixed pages, so those get a document score without a page map. Scanned, image-only PDFs have no selectable text to extract, and this tool doesn't OCR them.
From there, the same language model that powers our core detector runs across the extracted text. It weighs the statistical patterns that tend to separate machine-written prose from human writing — how predictable each word is given the ones before it, how evenly sentences are paced, and how little the rhythm varies from line to line. Human drafts are messier: they wander, hedge, repeat a favourite phrase, and break their own pacing. Generated text is usually smoother and more uniform than a person writing under deadline ever is.
Each page is then sent through the detector on its own, which is why the page numbers can disagree with each other and with the document score — they are separate reads, not slices of one average. Pages with almost nothing on them (a title page, a references list, a half-finished last page) are reported as unscored rather than given a number the detector can't stand behind.
Paragraph checkers are fine for a quick gut-check. This tool is for the people who get handed finished documents and have to make a call on them:
If you are a student, check your paper before your teacher does. Upload the PDF or Word file you are about to submit, see which pages read as AI, and rewrite those parts in your own words. Keep your drafts and version history: they show how the work was written, which a score cannot. No detector is 100% accurate, and writing by non-native English speakers is flagged more often (Liang et al., Stanford, 2023), so read a high score as a reason to look again, not a verdict.
If you are a teacher, use the page map to decide where to read closely, then print a one-page summary of the result to talk it through with the student. A score is a starting point for that conversation, never proof on its own; ask about drafts and sources before drawing any conclusion. Checking a whole class? The TextSight app takes up to 25 files in one batch on Pro.
A per-page score is an estimate of how AI-like that page reads — not a confession and not proof. The most useful thing on the screen often isn't the headline number; it's the shape of the scores across the document. A draft where the writer leaned on a model for a tricky middle section will usually show a spike on those pages and lower numbers around them. A document that's uniformly high, or uniformly low, is a different story than one that swings.
Treat the result as triage. Let it tell you which pages deserve a careful read, then judge those pages yourself — checking whether the voice matches the rest of the work, whether the claims hold up, and whether the writer can talk through what they wrote. For anything with real consequences, the score should start a conversation, never end one.
No AI detector is infallible, and document-level checking has its own quirks worth knowing. Very short pages — a title page, a half-finished page, a references list — give the model little to work with, so their scores carry less weight than a dense page of prose. Heavily edited text, where someone generated a draft and then rewrote it in their own words, can read as human because, by that point, it largely is. And tables, citations and boilerplate can nudge a score in either direction without telling you much about the author.
A couple of habits keep you on the right side of all this. Don't act on a single page in isolation — read the document's pattern. Don't treat a high score as a verdict against a person, especially a non-native writer, whose plainer phrasing can look more uniform than it is. And if a result matters, pair it with the things a detector can't see: the writer's process, their drafts, and a quick conversation. Used that way, per-page scoring is a fast, honest first pass — not the last word.
Why a page-by-page map beats a single document percentage, what the scoring caps mean on each plan, and the questions worth asking any AI document detector before you trust its number — in the full AI Document Detector guide.
Read the guide →Paste the text into the full AI Detector for line-by-line analysis.
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