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AI Writing Checker

Measure the patterns that make text read as machine-written: sentence-length variation, overused vocabulary, hedging and em dash density.

What an AI detector can and cannot tell you

This tool measures the writing patterns that make prose read as machine-generated, and reports each one with the evidence behind it. It does not output a percentage chance that a model wrote your text, because that number cannot be produced honestly and the harm from producing it anyway is real.

Here is the problem with AI detectors. They are statistical classifiers guessing at authorship from surface features, and they are unreliable enough that OpenAI withdrew its own for low accuracy. Their false positives fall hardest on people writing in a second language, whose prose is measurably more regular and therefore looks more machine-like to a classifier. A confident-looking "87% AI-generated" gets used to accuse a student or reject a freelancer, and there is nothing underneath it that can carry that weight. Any tool that shows you such a number is inviting you to make a consequential decision on a coin flip.

What can be measured honestly is the text itself. Sentence-length variation, usually called burstiness, is the strongest single signal: human writing mixes a three-word sentence with a thirty-word one, while machine drafts settle into a uniform middle length. Vocabulary density counts how often the text reaches for the small set of words models overuse. Hedge density counts phrases like "it is important to note that" which add length without meaning. Em dash rate counts a character models produce several times more often than someone typing. Connective openers count sentences that begin with Moreover or Furthermore.

Each of those is a fact about the writing rather than a claim about the writer, and each is reported with the matches that produced it, so you can check the measurement rather than trust it. That makes the result useful for the thing it is actually good for: editing. Low variation and heavy hedging make prose worse regardless of who wrote it, and both are fixable.

Use it to diagnose your own drafts, to see which parts of a document read as formulaic, and to understand what the patterns are. Do not use it, and do not let anyone use it, to decide whether a person is telling the truth about their own work. Everything runs in your browser; nothing is uploaded, logged or stored.

How to check writing for AI patterns

  1. Paste the text

    Any prose - a draft, an article, a submission. It stays in your browser and is never uploaded.

  2. Read each signal

    Six measurements, each with its value, a plain-English explanation, and the matches that produced it.

  3. Check the evidence

    Every signal lists what it actually matched. If a count looks wrong, the evidence tells you why.

  4. Fix what is worth fixing

    Low variation and heavy hedging make writing worse whoever wrote it. Send the text to the humanizer, or edit by hand.

What it measures

  • Sentence-length variation (burstiness)

    Reported as a coefficient of variation, so it does not depend on absolute length. The strongest single signal in the set.

  • Overused vocabulary density

    Counts around 40 words models reach for constantly, normalised per 1000 words so short and long texts compare.

  • Hedge and opener counts

    Phrases that add length without meaning, and sentences that begin with connective throat-clearing, both listed with matches.

  • Evidence for every number

    Each signal shows the actual matches or the raw sentence lengths, so a measurement can be checked rather than believed.

Frequently Asked Questions

No, and neither can any other detector, however confident its output looks. This measures writing patterns, not authorship. Treat a tool that gives you a percentage as guessing, because it is.

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