Free AI Detector for Teachers.
Honest classroom checks without an institutional license. A score opens a conversation with a student. It should never close one.
Scores 40 to 69 are inconclusive. Never punish on a score alone.
A fair workflow for AI suspicion
You read forty submissions a week and some of them feel off: too smooth, no voice, nothing like the student’s in-class writing. A detector gives that feeling a number, which is useful as long as the number keeps its place. Here is the workflow that survives contact with an appeals process. Scan the suspect passage. Treat readings below 70 as no actionable information. For high readings, compare against the student’s known writing, check for invented citations, then talk to the student and ask for process evidence: drafts, notes, version history. Decide on the totality, document your steps, and never let the score be the only line in the report.
Non-native English speakers, formal careful writers, and heavy grammar-tool users. Peer-reviewed studies have measured elevated false positive rates for non-native writers across all major detectors. If your roster includes them, weight process evidence over scores even more heavily.
Why honest tooling matters more than strict tooling
Vendors sell certainty to institutions because certainty is what procurement wants to buy. But several universities have publicly stepped back from detector-driven enforcement after false accusations, and the pattern in those cases is always the same: a score was treated as a finding. A tool that shows you its inconclusive band, like this one, is harder to misuse. The dial here marks 40 to 69 as no man’s land on purpose, and the verdict copy says when the instrument does not know. That posture protects you as much as the student: an enforcement decision built on documented process survives review; one built on a percentage does not.
Building a classroom policy that works
Three elements make AI policies enforceable. Disclosure: tell students which tools you use and what triggers a closer look. Process: require or encourage version history on major assignments, which converts every future dispute into a two-minute check. Proportion: define in advance what a high reading leads to, and make the answer a conversation rather than an automatic penalty. For background you can hand to colleagues, the explainer on how detection works and the data in Turnitin accuracy are written for exactly that purpose.
Related tools and guides
Scan a submission.
Then do what good teachers do: ask the student about it.
Free. No account. Nothing stored.Frequently asked
Can I use this score in an academic integrity case?
Use it as a starting point for a conversation, never as the evidence. Detector scores are probabilities with known false positive rates, and several universities have paused detector-based enforcement for exactly that reason.
What score should make me look closer?
Treat 70 and above as worth a closer look, 40 to 69 as no information, and below 40 as leaning human. Then look for the real tells: voice mismatch with in-class writing, invented citations, and suspiciously uniform paragraphs.
Which students are most at risk of false flags?
Non-native English speakers, students who write in a formal, careful register, and anyone using grammar tools heavily. Published research has documented elevated false positive rates for non-native writers across detectors.
What is a fair classroom policy?
Tell students which tools you use, allow process evidence (drafts, version history), and never convert a score directly into a penalty. A score opens a dialogue; documents and drafts close it.