AI Detector Checker is a free AI detector that reviews any text for AI-like writing signals. You paste your text, the tool analyses it, and it returns an AI-likeness score together with sentence-level highlights so you can see which parts of the writing look more machine-like. This page explains, in plain language, what happens between pasting your text and reading the result — and, just as importantly, what the result can and cannot tell you.
Using the tool: step by step
- Open the detector. No account or sign-up is required. You use the detector through the web interface; submitted text is sent securely to our server for analysis and processed as described in our Privacy Policy.
- Paste your text. Copy your text into the box, or type directly. The tool accepts multilingual input. A text-language menu is available as an analysis setting; it tells the tool which language to assume and is not a claim of validated accuracy for that language.
- Start the review. The tool first checks that the text meets the minimum and maximum length it can work with, then runs its analysis. Processing time varies with text length and server load; the result is shown when processing is complete.
- Read the result. You see an AI-likeness score from 0 to 100, an auxiliary signal agreement reading that describes how much the individual signals agree with each other, and sentence-level highlights marking passages with stronger AI-like writing signals. Use these to decide which sections deserve a closer human read — not to reach a verdict about who wrote the text.
What happens behind the scenes
The detector uses a multi-signal approach. Instead of relying on any single test, it measures several independent linguistic and statistical patterns and then reports them as auxiliary context, while a document-level classifier produces the final AI-likeness score; these visible indicators are not averaged into that score. Here is the general idea behind each stage.
- Receiving and validating the text. The tool checks the input format and length. Text that is too short carries very little signal, so the result for short passages should be read with extra caution.
- Predictability (pseudo-perplexity). Using a multilingual masked language model, the tool estimates how predictable the wording is. Text whose words are consistently easy to anticipate tends to score as more AI-like, while writing with more unexpected word choices tends to score as more human-like. This is a signal, not a measurement of authorship.
- Semantic patterns. The tool compares how sentences relate to one another in a shared multilingual embedding space. Writing that is unusually uniform in meaning and structure can look machine-like; more varied, uneven flow is more typical of human writing.
- Statistical patterns. The tool looks at language-independent measures such as variation in sentence length (burstiness), word-repetition patterns, and punctuation rhythm. These checks work on the raw structure of the text rather than its meaning, which is why multilingual input can be accepted.
- Combining the signals. The final AI-likeness score (0 to 100) is produced by the document-level classifier. The visible perplexity, semantic, and statistical indicators provide auxiliary context and are not averaged into the final score. The tool also reports Auxiliary signal agreement, which describes only how closely those three visible auxiliary indicators align with one another; it is not confidence, accuracy, or proof of authorship. When the signals disagree, the result is more ambiguous and deserves more careful interpretation.
- Highlighting sentences. Sentences that show stronger AI-like writing signals are highlighted so you can see where independent sentence-level cues are stronger; they do not explain, decompose, or determine the document-level classifier score. A highlight points to a pattern in that sentence; it is not independent proof that the sentence was machine-written.
- Interpreting in context. The result should be read together with what you already know about the text — its length, language, subject, genre, and editing history. Heavily edited AI text and carefully structured human text can both sit in the middle of the range.
We describe the general methodology openly, but we do not publish the exact internal weightings or thresholds that would make the tool trivial to game.
Limitations you should keep in mind
- The tool accepts multilingual input, but its performance has not been publicly validated for every language, dialect, domain, or text type. Results may vary by language, writing style, and text length.
- Short texts, quotations, lists, and translated or heavily edited passages are harder to assess and can produce misleading scores in either direction.
- A high score is not confirmation that AI wrote the text, and a low score is not confirmation that a human did. The tool cannot identify which specific person or AI system produced a piece of writing.
The score is a pattern-based review signal. It is not proof that a specific person or AI system wrote the text.
Now that you know how it works, try the free AI detector on your own writing. Treat the result as one input for review alongside your own judgement — never as a conclusion about who wrote the text.