AI detector for Professors

1. 1

   Receive draft from graduate student, research assistant, or peer reviewer

2. 2

   Run through AI Checker for quick screening

3. 3

   Review the sentence-level breakdown for specific suspicious passages

4. 4

   Cross-check flagged sections against domain-specific knowledge

5. 5

   Use detection result as one input among many in editorial decisions

The professors use case, in detail.

Professors and academic reviewers face a different problem from K-12 teachers: graduate-level writing is supposed to be formal and precise, which makes AI detection harder. Real human academic writing scores 25-45% on most detectors as a baseline simply because of the formal register. AI Checker addresses this by reporting calibrated confidence intervals rather than raw percentages, and by exposing the underlying signals (perplexity, burstiness, lexical fingerprint) so reviewers can interpret the score in context. For graduate program use, we recommend treating scores below 50% as inconclusive, scores between 50-75% as warranting closer reading, and scores above 75% as worth a direct conversation with the author about authorship and tooling. The API tier supports batch submissions and integration with peer-review platforms.

The professors workflow, in depth.

Detection at the graduate and faculty level is a fundamentally different problem from undergraduate detection because graduate-level writing is formal by training. The signposted-thesis-supporting-evidence-conclusion structure that AI models reproduce is also the structure of competent academic writing. Real human academic prose routinely scores 25-45% AI on most detection tools as a baseline, and treating that baseline as suspicious is a recipe for false accusations. AI Checker's professor tier is calibrated for this reality. Rather than reporting a single percentage, we report confidence intervals that account for register: the same 60% headline score means different things in different contexts. For a master's thesis chapter, 60% is roughly baseline for the formal register and not a strong AI signal. For a personal-tone reflection essay, 60% is well above register baseline and warrants closer review. The professor tier exposes the underlying signals (perplexity, burstiness, lexical fingerprint) per submission so reviewers can interpret scores in context rather than relying on the headline number. Recommended thresholds for graduate-level work: scores under 50% are inconclusive and not worth following up on; scores between 50-75% warrant closer reading of the sentence-level breakdown to identify any unusually-shaped passages; scores above 75% are worth a direct conversation with the author about authorship and tooling, ideally framed as a check-in about workflow rather than an accusation. The API tier supports batch submission for whole-class screening, integration with peer-review platforms, and an audit log feature useful for compliance documentation in promotion-and-tenure committees. AI Checker is used by graduate programs in roughly 40 countries; we do not share submission data across institutions.

Built for professors 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.