AI detector for Researchers

1. 1

   Draft research output (paper, report, white paper)

2. 2

   Run through AI Checker as part of pre-submission review

3. 3

   Verify that any AI-assisted sections are properly disclosed

4. 4

   Check sentence-level breakdown for AI-shaped paraphrasing

5. 5

   Adjust any sections that read more AI than your authentic voice

The researchers use case, in detail.

Researchers face a complex landscape: many journals now require disclosure of AI assistance, but the specifics of what counts as "assistance" vary. AI Checker isn't a tool to detect rule-breaking — it's a tool to verify that your own AI use is properly disclosed and bounded. For multi-author papers, it can also help identify when collaborator-written sections include unattributed AI assistance, which is a more common issue than outright fabrication. The sentence-level breakdown is particularly useful for distinguishing AI-drafted sections (need disclosure or rewriting) from AI-edited sections (usually fine, depending on journal policy). Researchers who use AI for legitimate purposes — literature review summaries, proofreading, translation assistance — generally have nothing to fear from detection tools. The risk is using AI for substantive drafting and forgetting to disclose it.

The researchers workflow, in depth.

Academic and industry research environments have evolved more rapidly than most workflows in their relationship with AI tools. By 2026, most major journals require some form of AI disclosure (Nature, Science, NEJM, the IEEE family, ACM, and most ACL-track venues all have explicit policies), but the specifics of what counts as disclosable assistance vary widely. Some journals require disclosure of any LLM use; others only of substantive drafting; others permit and require disclosure of translation and proofreading separately. AI Checker is positioned as a verification tool for researchers in this landscape: it doesn't tell you whether your use was acceptable (that's a journal-policy question), it tells you what your final manuscript looks like to AI detection. Three workflows are common. First, pre-submission verification: run your manuscript through AI Checker before submitting to confirm that any AI use you've already disclosed matches the actual signal in the text — useful for ensuring the disclosure is honest and complete. Second, multi-author quality control: in multi-author papers, run sections written by collaborators to confirm they didn't include unattributed AI assistance that would create disclosure issues for the corresponding author. Third, post-acceptance auditing: some institutions now require post-publication AI auditing for high-profile work, particularly in clinical and policy-relevant domains. The sentence-level breakdown distinguishes AI-drafted sections (need disclosure or rewriting) from AI-edited sections (usually within most journal policies as proofreading) — this distinction matters because different signal patterns in the breakdown correspond to different journal-policy implications. Researchers who use AI for clearly legitimate purposes (literature review summaries, language editing for non-native English speakers, translation assistance) generally have nothing to fear from detection: those uses produce sparse, scattered signals rather than the document-wide patterns of substantive AI drafting.

Built for researchers who need actionable detection.

• Sentence-level breakdown. Every result includes per-sentence scoring with the most likely source model identified — so the output is actionable evidence, not just a single percentage.
• Free tier with no daily traps. Up to 10,000 characters per check, unlimited checks per day, no signup required. Paid tiers exist for volume and team features, not as a tax on the free experience.
• API access on every plan. Integrate detection into your existing workflow with a clean REST API. Documentation includes example clients in TypeScript, Python, Go, and Rust.
• Privacy-first by default. Submissions are processed in memory and not used to train models. No third-party advertising trackers. Read our security & privacy policy for the long version.
• Multi-model coverage. We detect ChatGPT, Claude, Gemini, Llama, Mistral, Microsoft Copilot, and emerging open-source variants — not just GPT.

Detection by model, comparison, and language.