Most detectors hand you a percentage and leave you to defend it. ScribeLens shows which sentences triggered the signal and why, so a conversation with a student can start from specifics instead of an accusation.
A number like "82% AI" tells you nothing you can act on. It does not say which paragraph looked unusual, what pattern caused it, or how confident the analysis actually is.
ScribeLens reports the same overall interpretation, but attaches sentence-level signals and reason keywords underneath it. If only the introduction and conclusion are flagged while the body reads as human, that is a very different situation from a uniformly flagged document — and you can see the difference immediately.
The hardest part of suspected AI use is the discussion that follows. Sentence-level evidence changes that discussion from "a tool says you cheated" to "these three sentences show patterns worth talking about — walk me through how you wrote them."
That framing is fairer to students and far more defensible for you.
It will not tell you a student cheated. AI detection is probabilistic pattern analysis, not proof — which is exactly why ScribeLens surfaces sentence-level evidence and reason keywords instead of a bare score, especially for formal academic prose, heavily edited work, and writing by multilingual students, where surface patterns can look deceptively uniform.
Treat the output as one input alongside draft history, prior submissions, and a conversation with the student. A policy built on a bare score, with no evidence attached, is hard to defend and unfair to the student it's applied to.
Be confident about what detection actually contributes, because it's real: AI writing leaves measurable statistical patterns — uniform sentence rhythm, predictable word choice, formulaic paragraph structure — and ScribeLens is built to surface exactly those patterns, sentence by sentence, with reason keywords naming what triggered each one.
Be equally clear about what it doesn't contribute. It doesn't identify which AI tool was used, doesn't check plagiarism, and a result is a probability for you to weigh, not a finding that settles a case on its own. Treat it the way you'd treat any other piece of evidence: alongside drafts, prior submissions, and the student's own account, feeding into a decision that's still yours to make.
Start with the graded verdict, then open the sentence view before you treat the headline as the whole story — that's where the actual evidence lives.
You don't need a formal policy overhaul to use a detector fairly — a short, consistent sequence applied to every result is enough:
The difference between a fair process and an unfair one usually isn't the tool — it's how the result gets used once you have it.
Not every scan lands on a clear verdict. ScribeLens reports graded outcomes — including "mixed signals" and "blend of human and AI writing" — for exactly this reason: some drafts genuinely don't show a uniform pattern, and forcing that into a single confident label would be less honest, not more.
An uncertain result is neither exoneration nor confirmation. Look at whether any individual sentences carry flags even inside an otherwise mixed document, and weigh the same context that matters for any result — formal register, heavy editing, or multilingual writing can all produce a less clear-cut read. Treat an uncertain result as a reason to rely more on the conversation and less on the score, not as a reason to escalate.
However a result comes back, the conversation is the part that actually matters. Lead with the specific sentences and reason keywords, not the headline number, and ask the student to walk you through how those passages were written before you draw any conclusion.
For a full workflow — reading the evidence, weighing drafts and context, and having the conversation itself — see how to use an AI detector fairly with students.
Every sentence in a submission is classified individually as human-like, mixed, or AI-like, shown against a graded overall verdict. Flagged sentences carry reason keywords naming the specific pattern behind them, so you can see what actually triggered a result instead of working from a bare score.
No. Use it as supporting evidence only. Final decisions should include revision history, sources, prior work, and a conversation with the student.
It's a known failure mode across pattern-based detection generally: multilingual writers often use more formulaic structures that can read as flaggable. It's exactly why ScribeLens weighs sentence-level evidence instead of resolving everything into one score — always read that evidence, not just the overall number, before drawing a conclusion.
No. You can run a guest check without signing up. A free account raises your monthly token allowance and enables PDF report exports.
Yes. Reference lists, citations, and publisher boilerplate are set aside automatically so they do not distort the verdict.
Yes — this page and the review workflow above are built around a classroom use case: reading sentence-level evidence on student drafts, not a bare score. The underlying detector is the same one students use to self-check their own work before submitting.
Paste any student draft and see the sentence-level signals before you draw a conclusion. Free, no signup needed.