Bulk-scan a whole class in minutes, get sentence-level evidence for honor-code talks in about thirty seconds, and a defensible PDF report you can hand to a department reviewer. Calibrated for ESL writing so structured English does not get over-flagged. FERPA-aware, GDPR-aware, and student text is never used to train the classifier. Free to try. No card.
For K-12, middle and high school, and undergraduate teachers grading mixed-ability classes under time pressure with a department policy that now asks for evidence rather than instinct.
You are not trying to catch every AI essay. You are trying to spend less time wondering and more time teaching. TextSight is built around that priority: short scan, clear evidence, easy export, and a tone that respects student context.
Five-paragraph essays, in-class writing, take-home assignments. The free tier covers casual sanity-checks on a single suspect paper. Pro at $19.99 a month (or $14.99 a month on yearly) covers the steady weekly load and adds the 90-day history that matters when a grade gets contested two weeks later.
Larger classes, longer essays, more pressure for documentation. Bulk upload of a class folder is the unlock here. Pro handles individual teachers. Business at $39.99 a month with 5 seats is the right fit for a department coordinating across instructors and TAs.
You need consistent evidence formatting across multiple teachers, shared history for moderation panels, and an audit log when a case escalates. The Business tier shares scan history across a workspace and produces the one-page PDF format reviewers want to see.
TextSight is not a Turnitin replacement. It is the same-class scan that runs while you grade, so you have sentence-level context before the official institutional report lands a day or two later.
Bulk-upload the class folder of PDF or DOCX submissions. TextSight returns a class dashboard in a few minutes with Authenticity Scores per essay. Anything below 50 is surfaced for a longer look. Pro and above.
You see the Authenticity Score next to the student name. Above 75, grade normally. Between 50 and 75, glance at the sentence highlights and factor them into your contextual read. Below 50, the essay gets a slower review with the full report open.
One-click PDF export per scan when you need a record. The PDF carries the student text, the score, the sentence flags, the timestamp, and the classifier version. This is the format department reviewers want, not a screenshot of a percentage.
Compared to reading every essay cold, suspecting AI in some, and escalating without evidence, the scan-then-grade pattern usually costs 30 to 45 minutes for a class of 25 essays and saves the painful escalations.
Verified institutional emails get an education discount on Pro automatically at signup. Department licensing through Business. Full details on the pricing page.
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A percentage by itself is not a basis for a conversation. The TextSight result panel shows where the classifier reacted and why, so you can read the evidence the same way the student can.
Every sentence is colour-coded by its individual AI-likeness score. Red sentences clustered in one paragraph are a stronger AI signal than scattered yellows. Scattered yellows in otherwise structured prose often mean the student writes formally and is not using AI. You read the pattern, not just the headline.
Each paragraph is rolled up into a card showing its own score and the dominant signal driving it. Useful when you want to ask the student about a specific section without scrolling through highlights.
The underlying signals the classifier weighs are surfaced read-only on Pro. Low perplexity plus low burstiness across the whole essay is the classic AI fingerprint. Mixed perplexity with normal burstiness is the human pattern. These are diagnostic context, not verdicts.
Two scores side by side. Authenticity Score is the inverse-AI reading. Plagiarism Risk is a separate signal that catches copied passages. A clean essay scores high on authenticity and low on plagiarism risk. The two together give a fuller picture than either one alone.
Native LMS plugins are not shipped yet. Here is the honest 2026 picture of what works today and what is coming.
Export student submissions as PDF or DOCX from Canvas, Blackboard, Brightspace, Google Classroom, Schoology, or Microsoft Teams. Drag the folder into TextSight bulk upload. Get the class dashboard back in a few minutes. Copy scores back into your gradebook, or save the PDF report for your records.
For single essays, paste the text into app.textsight.ai. The free tier covers casual one-off scans up to 5,000 characters. Pro extends per-scan length and unlocks bulk upload.
One-click scan from any web page including hosted submission viewers. Useful when you are reading inline rather than downloading. Available on Starter and above.
Canvas, Google Classroom, Blackboard, Brightspace, Schoology, and Microsoft Teams plugins are on the roadmap. We are not promising dates while the integration partners change their plugin requirements; we will not ship a thin wrapper that breaks every term.
Student submissions are protected by FERPA in the US, by GDPR in the EU and the UK, and by local equivalents elsewhere. TextSight is designed to honour those rules out of the box.
Student text submitted for scanning is never used to train the classifier or any other model. This is a contract clause, not a setting you have to find.
Any scan can be deleted from history. On Pro you can delete individual records. On Business, deletion can be applied across a workspace by an admin in a single action.
Business and Enterprise tiers ship with a standard Data Processing Agreement. Larger institutions with custom DPA needs are handled via the contact form, usually inside a week.
Hosting is on Hetzner in Germany for ML inference and on DigitalOcean for the API. EU institutions get EU residency by default. For US institutions that require US-only residency under FERPA, the Enterprise tier is where that is contractually scoped.
Treating any single number as proof a student used AI is unfair and unreliable. The 2026 expectation is that teachers use detection as one input among several, and the design here is built around that expectation.
A low Authenticity Score means the essay reads more AI-like to the classifier. It does not mean the student used AI. False positives are real, particularly for ESL writers, formally-taught Oxford-style English, and standard topics where phrasing overlaps with AI defaults. Your contextual judgment is the final layer, not the classifier.
Department policies that auto-fail on a single detector percentage produce bad outcomes and have already led to lawsuits in the US and UK. The defensible path is conversation first, evidence second, decision third, with the scan as supporting context throughout.
The sentence-level highlights are the lever. Ask the student to walk you through how they wrote a specific flagged paragraph. Ask for earlier drafts or research notes. The flags give the student something specific to respond to, which is fairer than a vague accusation based on a percentage.
Deeply-rewritten AI text is hard for any detector to catch reliably. The durable defences are assignment design: in-class drafting, multi-stage submissions with conferences, prompts that require personal experience or course-specific knowledge. The detector is one signal in that ecosystem, not the whole defence.
Full overview of the teacher landing page, with classroom-facing examples and screenshots.
For teachers →Department and institution-level licensing, audit logs, and DPA for academic compliance teams.
For universities →The detector ranking your students are already comparing. Useful context for grading-side decisions.
See the ranking →Free, Starter, Pro, Business. Yearly billing saves 25%. Education discount applies on Pro at signup.
See pricing →Free to try. No card. Education discount on Pro for verified institutional emails.