Review text with transparent writing-pattern signals and practical human-review guidance for essays, articles, editorial drafts, and everyday writing.
🔒 Local browser review — no signup and no text upload
Built for writers, educators, editors, and content teams
Paste a representative passage to inspect sentence variation, vocabulary diversity, and repeated terms without uploading your text.
Your text stays in this browser. This review measures visible writing patterns; it does not identify an author, prove AI use, or send your text to a model.
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Model names provide context, not a verdict
AI Detector is a review workspace for writers, educators, publishers, and content teams. It helps people examine visible writing patterns and document a careful follow-up process. Automated signals are never proof of authorship or intent; context, source checks, and human judgment remain essential.
The local review surfaces sentence rhythm, vocabulary diversity, and repeated terms so a person can decide what deserves a closer read.
The on-page writing review runs in the browser, so a pasted sample is not sent to a model or stored by this tool.
ChatGPT, Claude, Gemini, and other model names provide context for a review, but no text pattern can prove which model—or person—created it.
Educators can use a signal to start a documented conversation, then rely on drafts, sources, policy, and an appeal path before reaching a conclusion.
A useful review process is transparent about what a signal can show and what it cannot. AI Detector keeps the first step local and makes it easier to document the human checks that should follow.
Use a complete passage rather than a headline, quotation, or isolated sentence. Short samples and formulaic writing make any automated interpretation less informative, so the surrounding task and audience should always be recorded.
Sentence rhythm, repeated terms, and vocabulary diversity can help a reviewer decide where to read more closely. They do not tell you who wrote a passage, whether assistance was permitted, or what action should follow.
For an editorial, academic, or employment decision, combine the writing review with drafts, citations, version history, policy, and an opportunity for the author to explain their process.
The local review highlights visible patterns in the text. It is designed to support a human workflow, not to produce a model attribution or a final authorship decision.
Include enough original prose to show normal sentence variation. Exclude quotations, templates, and reference lists when they are not part of the writing you need to review.
Paste the passage into the on-page tool. It summarizes sentence length, sentence variation, vocabulary diversity, and repeated terms entirely in the browser.
Compare the observations with sources, drafts, assignment rules, style guidance, or the documented purpose of the content. A writing pattern cannot establish authorship on its own.
For a draft, that may mean substantive editing or fact-checking. For a sensitive review, it may mean a conversation, a second reviewer, or a documented appeal path.
These visible language characteristics are useful for deciding what to inspect next. They are deliberately presented as review context, not as an AI probability score.
An unusually uniform sequence of very short or very long sentences can be a useful prompt to read a passage in context.
Variation shows how much sentence length changes across a sample. It describes the text; it does not identify a writer or a model.
This compares the number of distinct words with the total word count. Topic, genre, translation, and accessibility needs can all affect it.
Repeated high-frequency terms can flag generic or overused phrasing that may deserve editing, source checking, or a closer human read.
The review runs in the current browser session. The text remains on the device instead of being sent to an external model by this page.
The most useful outcome is a clear next step: edit a passage, verify a source, ask a question, or note why no action is needed.
Responsible AI-assisted writing review has the same foundation in every setting: explain the purpose, preserve relevant evidence, and avoid treating a text pattern as a conclusion. These principles help teams use automation without turning it into a black box. For a classroom essay, that means recording the assignment rules and inviting a student to explain their work. For a publisher, it means checking sources, reporting, expertise, and the editorial brief. For SEO, it means assessing whether a page answers a real user need with original, accurate information. In every case, a review should be proportionate to the stakes and leave a record of what was considered. This approach keeps later decisions accurate, consistent, and respectful.
Tell writers, students, contributors, or employees what is being reviewed, which policy applies, and how the result will be used before collecting their work.
Use sources, draft history, citations, factual checks, and subject-matter review to test an observation. Repeating the same automated review does not create new evidence.
If a review could affect a grade, publication, payment, or opportunity, give the author a chance to explain and use a consistent escalation or appeal process.
The tool is designed to make its limits clear so that people can use it thoughtfully in a broader writing-review process.
Start with the local writing-pattern review, then use human judgment and independent evidence to decide what deserves attention.