AI Detector - Free AI Checker for ChatGPT, GPT-5 & Gemini

What

GPTZero is an AI content detection and writing analysis platform for checking whether text is likely to have been generated or altered by large language models such as ChatGPT, GPT-5, Gemini, Claude, and Llama. It is used by educators, students, writers, recruiters, publishers, and other organizations that need to assess authorship, originality, and writing quality.

The core workflow is straightforward: paste text or upload a document, run a scan, review the predicted AI likelihood and highlighted passages, and optionally use deeper verification tools such as advanced scanning, writing replay, plagiarism checks, or grammar feedback. Based on the page, GPTZero appears positioned as a commercial AI detection product with strong emphasis on education and institutional use, while also serving broader professional writing and review workflows.

Features

• AI text detection: Scans pasted text or uploaded files to estimate whether content was generated by AI and identifies passages that may be machine-written.
• Advanced AI Scan: Provides more in-depth probability analysis for users who need stronger review signals than a basic scan.
• Writing Replay and authorship verification: In Google Docs, it can show the writing process, including copy-paste activity and typing patterns, to help verify whether work was written naturally.
• Hallucination and citation checking: Includes a hallucination detector aimed at spotting fake academic sources or hallucinated citations.
• Plagiarism and grammar tools: Combines originality checks with grammar and spelling feedback so users can review both authenticity and writing quality in one workflow.
• Integrations and deployment options: Offers Chrome extension, Google Docs, Canvas, API, and Zapier support to embed detection into classroom, browsing, or operational workflows.

Helpful Tips

• Treat detection as a review signal, not a final verdict: Even when a vendor reports high accuracy, AI detection should support human judgment and follow-up review rather than serve as sole evidence.
• Prioritize transparency features for sensitive use cases: Writing replay, typing patterns, and document history are generally more actionable in disputes than a standalone percentage score.
• Match the product tier to document volume and process complexity: Basic scanning may be enough for occasional checks, while batch scanning, LMS support, and API access matter more for institutional deployment.
• Test multilingual and mixed-authorship scenarios early: If your environment includes non-English writing or documents edited by both humans and AI, validate performance against your own sample set before broad rollout.
• Use writing feedback carefully in education settings: When a product combines detection with tutoring or grammar support, clear policy guidance helps separate learning support from integrity enforcement.