Language support matters because writers, editors, localization teams, and researchers work across many languages. An honest AI detector has to say which languages its results have actually been validated for, and AI Detector Checker’s answer is specific: the current validation is strongest for English text, and other languages have not been validated for the current detector. Use the tool only for low-stakes review of your own text or text submitted with the writer’s informed consent. Do not use it to evaluate a person or make, support, influence, or trigger a high-impact decision.
AI Detector Checker is a conservative AI-writing-signal flagger for English text of at least 100 words. It returns a 0-100 AI-writing signal score with a stability range and one of three bands. Text in other languages is not rejected, but results on it are unsupported: they have not been validated for the current detector and should not be read as evidence of anything. Even for English, results are review signals, never proof of authorship.
Try the AI detector when you want a fast first-pass review of English text with no sign-up required, then use this page to understand what language support really means in practice.
Table of Contents
What This Page Covers
This page explains which language the current detector has been validated on, what happens if you submit text in another language, why there is no per-language mode, and how translation, code-switching, and non-native writing affect interpretation.
It is designed for people who want practical answers, not vague promises. If you want to know whether your language is covered by the validation, whether translating text into English before checking it is a good idea, or how language background can affect a result, this page is the right starting point.
What Language Support Means for the Current Detector
The current detector was built and validated on English text. Its final sealed confirmation used 2,604 human-written English texts from 25 sources and English AI-generated text, and its input rules (a minimum of 100 words, counted as space-separated tokens) assume English-style writing. The tool does not block other languages, so you can paste non-English text and receive a score and band, but nothing about that result has been checked.
Accepting a language is not the same as validated accuracy in it. We publish no per-language figures because there is no evaluation to support them: the current detector has not been validated for any language other than English, and its behaviour on other languages, dialects, and scripts is unknown. Results on non-English text should be treated as unsupported output, not as a weaker version of an English result.
This distinction matters because users often assume “accepted” means “tested.” It does not. For how the current detector was validated and where the evidence stops, see the validation evidence and limitations page.
Is There a Per-Language Mode?
No. The current detector runs one analysis regardless of language; there is no validated language-specific mode. If the form shows a language menu, it is a convenience inherited from earlier versions and does not change how the current detector reads your text. Languages you may see listed there include:
- English
- French
- Spanish
- German
- Italian
- Portuguese
- Dutch
- Polish
- Russian
- Chinese
- Japanese
- Korean
- Arabic
- Other Language
Read that list as a list of languages the form will accept text in, not as a list of languages with validated detection. Only English has been validated. Choosing a language does not change the analysis and does not certify how the detector performs on that language.
Because the current detector has not been validated on any language other than English, treat a non-English result as unsupported: it is not evidence about who wrote the text, and it should not be used even as a soft signal in a review.
Why We Do Not Offer Language-Specific Detection
A per-language mode would only be honest if each language had its own validated evidence: its own sealed set of human writing from many sources, its own AI text from several model families, and its own false-flag measurements. The current detector has that evidence for English only. Offering a Spanish or Arabic mode without it would imply a level of testing that does not exist.
The detector is also deliberately conservative: it is tuned so that strong flags on human writing stay rare on the English validation sets. That tuning was measured on English. On another language the false-flag rate could be higher or lower, and nobody knows which, which is exactly why the result is unsupported rather than merely “less accurate”. If you want a closer look at the basic check flow, see how AI Detector Checker works from input to result.
None of this removes the need for judgment even in English. If a passage is short, mixed-language, unusually formal, translated, or written as a reply to an instruction, interpretation still requires care, and no result should be treated as a shortcut to certainty.
How AI Detector Checker Fits Multilingual Workflows
AI Detector Checker produces a document-level AI-writing signal score with a stability range and a band for the whole passage; no individual sentences are marked. In a multilingual workflow its role is narrow and honest: it can give a conservative first-pass signal on the English material in that workflow, and it should be left out of the loop for material in other languages.
In permitted workflows that looks like this: a writer may review their own English draft; an editor may review an English translation submitted with informed consent, remembering that the translation, not the original, is what was checked; a marketing team may review authorized English landing pages. For the Arabic, Spanish, Japanese, Korean, or French material in the same projects, the responsible choice is human review without the detector. In every case the output remains a low-stakes revision signal, not a verdict about a person or authorship.
The current detector marks nothing inside a passage, so a document with translated or mixed-language sections needs to be read by a person rather than mapped by the tool. For a user-facing description of what the product does and does not show, see the features page.
Translation, Code-Switching, and Non-Native Writing
Even within English, interpretation becomes harder when the text is translated, switches between languages, or reflects non-native writing patterns. These are not edge cases anymore. They are common parts of real-world review.
Translated text can introduce more standardized phrasing, smoother syntax, or less local rhythm than original-language writing. That can affect how the detector reads the text. A translation may sound more uniform even when it was prepared by a human translator or editor.
Code-switching can create ambiguity because the stylistic and structural signals are no longer coming from a single language system. A draft that moves between languages naturally may still look less predictable to a detector in some places and more standardized in others.
Non-native writing can also influence interpretation. Predictable phrasing, simpler transitions, or more formal constructions may reflect language background rather than machine authorship. In the current detector’s development evidence, English written by learners was among the registers with an above-average false-flag rate, which is why such text needs a careful, human-led reading of the result rather than a reflexive conclusion.
These are reasons to interpret results on translated and non-native English more carefully, and reasons to keep non-English text out of the detector altogether. If you want a broader discussion of ambiguity, edge cases, and false-positive context, the page on AI detector limitations and false positives is the right companion resource.
What Affects Reliability Across Languages
Some checks are easier to interpret than others. Reliability depends on the language (only English is validated), the kind of text, the amount of context, and the way the document was prepared.
- Short text: passages under 100 words are refused rather than labelled, because the detector has no validated behaviour there.
- Translated text: translation can change rhythm, phrasing, and predictability.
- Non-native writing: predictable constructions may reflect language proficiency rather than AI generation.
- Mixed-language documents: switching languages can create uneven signals across sections.
- Highly formal or template-like writing: standardized prose may look more machine-like regardless of language.
- Technical or domain-specific writing: specialized summaries can be dense, repetitive, or structurally narrow.
- Uneven document sections: an introduction may read differently from the main body or the closing section.
- Lesser-context snippets: isolated paragraphs are harder to assess than fuller documents.
- Dialect variation and localized idioms: regional language patterns can make interpretation more nuanced.
The practical takeaway is simple: English is validated, other languages are not, and even in English interpretation depends on the writing situation. Results are review signals, not definitive judgments, and one check should not be treated as proof in high-stakes scenarios.
When to Scan Text in Its Original Language
Translating a non-English text into English so the detector can read it is not a workaround. Translation changes the texture of the writing: it can smooth transitions, standardize wording, and alter the balance between natural variation and predictable phrasing. A result on the translation describes the translation, and machine translation in particular can itself read as machine-like.
Because of that, a check on a translation should not be treated as evidence about the original. Translation may be helpful for human understanding, but it introduces a second layer of distortion into the review process, and the original-language text cannot be checked at all with the current detector.
If original-language review is difficult for your team, the better response is to add human context rather than lean harder on automation. That might mean involving a native speaker, checking the purpose of the document more carefully, or reading the passage against the rest of the writer’s work before drawing conclusions.
Who This Matters Most For
Clear language limits matter most when real workflows cross language boundaries. AI Detector Checker is relevant to these teams for the English part of their work, and its limits are relevant to the rest.
- Writers and students: for self-reviewing English drafts before sharing or submitting them.
- Researchers and localization teams: for de-identified comparisons or text intentionally shared for low-stakes editorial review.
- Editors and publishers: for reviewing consented English submissions and English translations before publication, with human review for everything else.
- Localization teams: for checking English source copy; adapted copy in other markets stays with human reviewers.
- SEO and content teams: for reviewing English landing pages, articles, and content assets.
- Business reviewers: for checking English internal communications, policy summaries, or customer-facing templates.
- Privacy-sensitive reviewers: for handling unpublished or internal text where confidentiality matters alongside language coverage.
If you want to see where these workflows show up in practice, the AI Detector Checker use cases provide broader examples of how different types of users apply the tool responsibly.
Privacy and Sensitive Multilingual Text
Language limits are only part of the picture. Privacy matters too, especially when the text is internal, unpublished, or sensitive. That is true whether the document is in English or any other language.
AI Detector Checker processes submitted text in-session to produce the result and does not store the submitted text. Even so, avoid pasting confidential, regulated, or personal information into any online tool. For the exact policy details, review the page on security and in-session text handling.
Best Practices for Reliable Checks in Multilingual Teams
A careful workflow in a multilingual team does not need to be complicated. It just needs to be realistic about what has been validated.
- Check English text only and route other languages to human review.
- Provide at least 100 words so the detector issues a label rather than asking for more text.
- Read the passage yourself instead of reacting only to the label; the tool marks nothing inside the text.
- Compare tone and specificity with the writer’s other work to see whether a flagged passage genuinely stands out.
- Be cautious with translated or mixed-language drafts because they can produce more ambiguous signals.
- Read the label responsibly by checking how to interpret AI Detector Checker results when you need help reading the output. The score and bands are not the chance of AI authorship, and “Likely human-written” is not evidence of human authorship.
- Keep edge cases in mind by using the broader AI Detector Checker FAQ for quick operational questions.
- Avoid treating one scan as final proof in any high-stakes review.
Better review in a multilingual team is not about forcing certainty from imperfect signals. It is about using the tool where it has been validated, combining its output with document context and human review, and leaving it out where it has not. If you want the broader story behind the product and its positioning, visit about AI Detector Checker.
FAQ
Does AI Detector Checker only work in English?
Effectively, yes. The current detector has been validated on English text only. The form does not block other languages, but a result on non-English text is unsupported and should not be used.
Do you publish accuracy figures for each language?
No. The only validated language is English, and the English evidence is published on the validation page. There are no per-language figures because no other language has been evaluated. Treat non-English results as unsupported.
Does choosing a language in the form change the result?
No. The form has no language selector: the detector runs the same single analysis on whatever text you paste. A language menu, where shown, is a leftover convenience and does not switch to a validated per-language mode.
Should I translate non-English text into English before scanning it?
No. A result on a translation describes the translation, not the original, and translated or machine-translated English can itself read as machine-like. Since the original cannot be checked with the current detector, the responsible option is human review.
Does accepting text in other languages mean the tool performs identically in every language?
No. Accepting input and having validated accuracy are not the same thing. Only English has been validated; on other languages the behaviour is unknown.
Can translated or mixed-language text produce ambiguous results?
Yes. Translation, code-switching, and non-native writing all affect interpretation even in English, which is why such results should be read with added care and why non-English text should not be checked at all.
Check Text With Better Expectations
Language support is only useful when it comes with clear expectations. AI Detector Checker gives you a conservative first-pass check for English text without pretending that other languages have been validated or that any result is final.
Start a free check and use the result as a better starting point for human review.
For what has and has not been validated for the current detector, see the model card.