AI Detection FAQs
Complete answers for AI checker accuracy, model coverage, mixed writing, PDF and DOCX files, resume detection, API use, LMS workflows, false positives, privacy, and responsible AI use.
An AI detector uses machine-learning systems related to the architectures behind generative AI. It does not generate words. It estimates whether each word, token, sentence, and full document is more likely to be AI-generated or human-written, then visualizes the result.
It is designed for text from ChatGPT, GPT-5, GPT-4o, Gemini 2.5, Claude 4.5, DeepSeek-V3, Qwen3, Llama, Grok, Copilot, and other current or emerging AI writing systems.
Yes. Mixed text is one of the main use cases. The detector looks for sections where human-created text may have been amended, expanded, paraphrased, or rewritten by AI.
A production version can support PDF AI detection and DOCX AI detection by extracting text from the file first, then running the same text-based analysis used by the editor.
Yes. You can paste resume or cover letter text into the editor. For high-volume recruiting, an AI resume detector API can connect to an applicant tracking system.
The page is structured for API, LMS, browser-selection, and enterprise workflows. Teams can use the same detection logic inside classroom systems, editorial tools, or screening pipelines.
The free checker can support quick scans with practical text limits. Paid plans can unlock longer character limits, saved reports, batch checks, file uploads, and team controls.
Very short text provides too little signal and can create false positives. Very long text needs token limits, processing controls, and fair-use boundaries, so large documents may be split or processed through an API.
Accuracy varies by text length, subject, language, editing level, and model family. Long-form documents usually provide stronger signals, while short or generic text can be harder to classify.
The best AI detector should combine model coverage, low false positives, explainable sentence evidence, transparent limits, privacy controls, and regular evaluation against fresh AI outputs.
Basic spelling, grammar, and clarity edits should not automatically mean a document is AI-generated. Heavy rewriting, generic phrasing, and uniform sentence patterns may increase the score.
A trustworthy AI checker should explain retention, encryption, access controls, training use, deletion options, and enterprise data-processing terms before teams upload sensitive writing.
AI detection can support review, but it should not be treated as automatic proof. Teachers should combine detection with draft history, citations, student discussion, and assignment context.
AI detection and plagiarism detection answer different questions. A full integrity workflow should also review source overlap, paraphrased passages, citation quality, readability, and factual claims.
AI spam is low-quality, repetitive, generic, or mass-produced machine-generated content. It often appears in reviews, social posts, comments, articles, and other user-generated content surfaces.
Yes. Some tools claim that text will pass a detector without actually checking it. Treat those claims cautiously and verify the final document with a transparent AI checker.
Search quality systems focus on usefulness, originality, expertise, and trust. AI detection can help editors find generic drafts, but strong content still needs accurate information and human value.
That depends on the policy. Schools, publishers, and workplaces should define acceptable AI assistance, citation rules, disclosure requirements, and review steps before judging a document.