AI detector for Teachers

1. 1

   Receive student submission (essay, report, take-home test)

2. 2

   Paste text into AI Checker — get probability score in seconds

3. 3

   Review sentence-level breakdown for specific flagged passages

4. 4

   If score is high, schedule a conversation with the student before any formal action

5. 5

   Use the breakdown as evidence + classroom context together

The teachers use case, in detail.

Teachers are the largest user group for AI detection tools, and the workflow demands more nuance than other use cases. A high AI score is not, by itself, evidence of academic dishonesty — it's a signal to look more carefully. AI Checker's sentence-level breakdown is specifically designed for the teacher workflow: it lets you point to specific passages in a follow-up conversation rather than presenting the student with a single percentage. Best practices: pair the score with classroom context (Did the student turn in earlier work in their voice? Did they show drafts? Does the topic match their stated interests?), avoid using AI scores as the sole basis for any disciplinary action, and treat any score below 80% as ambiguous — the false positive rate on real human writing is approximately 1-2% even for our system. Teacher accounts on AI Checker include batch submission, gradebook export, and a privacy-first mode that doesn't log student text.

The teachers workflow, in depth.

The teacher use case for AI detection is the most consequential and the most often misused. Educators have access to a tool that returns a single number summarizing whether a student's work might be AI-generated, and the temptation to treat that number as a verdict is strong. AI Checker's editorial position — informed by educator advisors who use the tool daily — is that the score is evidence in a review, never a final decision. The recommended workflow has four stages. First, screen submissions with the detector and treat any score above 70% as a flag for closer reading rather than a verdict. Second, read the sentence-level breakdown to identify which specific sentences scored highest; these are the passages worth discussing. Third, compare flagged passages to the student's prior work and observed in-class voice — if the flagged passages read in a markedly different register, that's a stronger signal than the headline number. Fourth, have a conversation with the student before any formal action, ideally framed as a check-in about their writing process rather than an accusation. Every step of this workflow is supported by features in AI Checker's teacher tier: batch submission for whole-class screening, sentence-level breakdown with copy-and-paste-friendly highlights for parent-teacher conferences, history tracking per student to detect drift over a semester, and a privacy mode that doesn't store student submissions beyond the current session. The teacher tier is free for individual educators; institutional licenses are available for districts and campus-wide deployments. We provide policy templates, sample parent communications, and a workshop curriculum for departments adopting AI detection at scale.

Built for teachers 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.