How to Check If Text Was Written by AI: Tools and Accuracy in 2026

A professor wants proof that you wrote your own essay. An editor suspects a freelancer handed in raw model output. In both cases the first move is the same: run the text through a detector and read the result carefully.

To check text for AI, paste a passage of several paragraphs into a detector such as Pangram. The service scores how predictable the word choices are and returns a probability, ideally marked up paragraph by paragraph. Accuracy depends on length: short snippets fool nearly every tool. One scan is a reason to look closer, not a verdict.

What does an AI text detector actually measure?

A detector does not hunt for watermarks and does not compare your text against a database of model answers. It estimates how predictable each next word is.

Language models drift toward safe, averaged phrasing. Sentences come out the same length, paragraphs follow the same shape, and the odd detail or abrupt turn never appears. People write unevenly. We add things that do not belong, cut a thought off halfway, and repeat a favorite word three times in a page. That unevenness is what gets measured.

Two consequences follow. A polished, formulaic human document — a report written from a template, say — can get flagged as machine-written. And model output that an author genuinely rewrote by hand often passes.

Which AI detector is the most accurate?

In practice Pangram holds up best. It is trained separately on different kinds of writing — essays, emails, posts, academic papers — and fires on real authors far less often than the free web forms do. It also returns a paragraph-level breakdown instead of a single number, so you can see which passage triggered the score.

What holds true for any service:

• false positives happen everywhere; the only question is how often;
• writing by non-native speakers has historically been flagged as AI more often, and weaker detectors still have this problem;
• mixed text, where a person drafted and a model edited, is the hardest case for every tool;
• the shorter the passage, the less the result is worth.

The subs-ai.com catalog carries annual access to Pangram Professional — the version with paragraph markup and no cap on individual checks, which is what matters most for a teacher or an editor.

How do you check a text for AI step by step?

1. Gather a passage of several paragraphs. Checking a single sentence tells you nothing.
2. Strip out boilerplate: title pages, tables of contents, bibliographies. They skew the picture.
3. Run the whole text at once rather than in chunks. Detectors weigh coherence across the document.
4. Read the paragraph breakdown if the service offers one. A uniformly high score across the whole text and one suspicious paragraph mean very different things.
5. Re-check the disputed passage with a second service. Matching verdicts make the signal stronger.
6. Collect outside evidence: edit history, drafts, and whether the author can explain their own reasoning out loud.

That last step outweighs everything above it. Version history in Notion or Google Docs shows the writing process, and a process is much harder to fake than a finished page.

Free detectors or paid services: what is the difference

For casual curiosity, free is enough. When a grade, a payment, or someone's reputation rides on the answer, you need a service that can explain its own decision.

What if your own writing gets flagged as AI?

This happens often, and it happens most to people who write tightly and to a structure. What to do:

• show the document's edit history — it is the strongest single argument;
• attach drafts, notes, and research sources;
• ask the reviewer to point to specific paragraphs instead of quoting an overall percentage;
• offer to rewrite the disputed passage while they watch.

Going forward, write in an editor that keeps versions, and avoid pasting model output in one block. If you do edit AI output, change the structure rather than swapping individual words. Structure is what detectors read.

FAQ

Can an AI text detector be fooled? Partly. Paraphrasers and humanizers lower the score, but serious detectors are trained on their output too. Rewriting the text yourself works better, because then it actually is yours.

Do detectors work on languages other than English? Multilingual services like Pangram handle several languages, though accuracy is usually highest in English simply because there is more training data. In other languages, trust the paragraph markup over the single summary percentage.

Can someone be expelled or fired over a detector result? The verdict is a probability, not a fact. It works as grounds for a conversation and a request for drafts, never as the sole proof.

Do detectors check code? Text detectors are not built for code. The signals are different there, and the question is normally settled through commit history. We cover development tooling in the AI and coding section.

What if I review other people's texts all day? Pick a service with paragraph markup and keep the reports. Notes per author help too, and annual access to Notion Business covers that.

The short version

AI detection works when you treat it as a filter rather than a court: it tells you where to look harder. Use one reliable service instead of five random ones, feed it enough length, and settle the arguable cases with edit history.

Annual access to Pangram Professional and other AI services — from Gamma for slide decks to Cursor for code — is in the subs-ai.com catalog. Worth a look if you are planning a year of subscriptions ahead.