What AI Text Detectors Can and Cannot Tell You

Teachers, editors and hiring managers now meet AI text detectors every week, often built into tools they already use. The tools report a percentage or a label, and it is tempting to read that number as a finding. This page explains what the number is, summarises what independent research has found about how reliable it is, and sets out a fair way to use a result. It does not rate any product; the principles apply to all of them.

How AI text detectors work

Most detectors are classifiers: statistical models trained on large collections of text labelled as human-written or machine-generated. Given a new passage, they estimate how closely it resembles each collection. Many rely on the observation that language models tend to choose likely words, so machine text is often more predictable, word by word, than human text, and more uniform from sentence to sentence. Some newer research systems instead look for a watermark: a hidden statistical pattern that a model deliberately builds into its word choices, which a matching detector can later test for. Watermarks only exist if the model that produced the text was built to add one.

What independent research has found

The published evidence is consistent on three points. First, detectors misclassify human writing, and they do so unevenly: a Stanford study found that essays by non-native English writers were flagged as machine-written far more often than essays by native speakers, largely because simpler vocabulary and structure look predictable. Second, paraphrasing defeats them: researchers showed that running generated text through a rewording step sharply lowers detection rates, and argued that reliable detection becomes harder as models improve. Third, people are not much better: in one widely reported test, scientists reading abstracts could not reliably tell generated ones from real ones.

None of this means a detector result is meaningless. It means a result is one weak signal, with known biases, that needs other evidence before anyone acts on it.

Why a reading record makes the score unnecessary

A detector score is a probability about style, produced after the fact by a tool that never saw the work being done. A reading record is the opposite kind of evidence: it is made while the work happens, by the person doing it, and every part of it can be opened and checked. The record has four parts, and none of them needs special software.

Put the two side by side and the difference is plain. A score says that a passage resembles other passages; it cannot say who typed it, when, or from what reading. A record says which pages were read, which words were quoted, when each draft existed and how it changed. A teacher who has the record does not need the score, and a teacher who has only the score does not have enough to act. The hub on highlights as evidence of reading and sourcing collects the methods, and keeping dated drafts with the revisions highlighted is the one to start with.

The record also settles the case where a detector has flagged honest work, because it can be examined without anyone having to trust a probability. Students who keep one from the first week of a course never have to assemble it in a hurry afterwards. The habits cost little: highlight as you read, date your notes, write in one place, and keep a link to each source.

Using a detector result fairly

1. 1

   Treat the score as a reason to look, not a finding

   A high score justifies reading the work more closely. It does not justify a conclusion, a grade penalty or a rejection on its own.

2. 2

   Read the passage yourself

   Mark the features that drew the score, using the approach in close reading for machine-writing habits, and look just as hard for specific detail only the writer could know.

3. 3

   Ask for the process

   Drafts, notes, version history and sources settle most questions quickly. The page on reviewing authorship through drafts and sources describes the conversation.

4. 4

   Record what you relied on

   If a decision follows, write down the evidence other than the score. If the only evidence is the score, there is no decision to make.

If a detector flags your own work

Stay calm and bring your process: dated drafts, the sources you read with your highlights, your notes, and your document's version history. Ask which passages were flagged and explain how you wrote them. Point out, politely, that published research documents false positives, especially for non-native writers. A clear record of your process is the strongest answer to any score.

Expert tips for anyone handed a score

• Ask what the tool measured. A percentage with no explanation of the method is a number without a meaning. If the vendor's documentation does not say what the score represents, treat it as unexplained.
• Ask for the record before the conversation. Drafts, marked sources and notes can be read in ten minutes and answer most questions; a conversation that starts from a score alone starts from nothing.
• Never act on a single passage. Formulaic prose is common in introductions, summaries and any writing by someone working in a second language. Look at the whole piece and at the sources behind it.
• Write down the evidence other than the score. If a decision follows, the file should show what it rested on. If the only entry is the score, the decision does not stand.
• Keep your own record too. If you write for publication, keep marked sources and dated drafts of every piece, so that a flag, whenever it arrives, is answered by opening a folder rather than by argument. Exporting highlights and notes so they can be handed over explains what the folder should contain.

Where this fits on the site

This site builds highlighting tools, not detectors, and the reading skills here are the part of the problem a detector cannot do for you. The tools and extensions section covers the highlighters and annotation apps that make close reading and process records practical, including annotation layers for shared reading and highlight links you can send as evidence.