For the middle and high school classroom: scan a class set of essays, get sentence-level evidence you can talk through with a student in plain language, and a clean PDF report for a parent meeting or a note to your department head. Calibrated so young writers and ESL students are not over-flagged for writing simply. FERPA-aware, GDPR-aware, and student text is never used to train the classifier. Free to try. No card.
For middle and high school teachers grading mixed-ability classes under time pressure, where the writing is shorter, the writers are younger, and a flag may end up in front of a parent or a principal rather than an integrity panel.
You are not trying to catch every AI essay. You are trying to spend less time wondering and more time teaching, and to handle the rare real case fairly when it lands. TextSight is built around that priority: a short scan, clear evidence a 14-year-old can follow, an easy export, and a tone that respects how young writers actually develop.
Paragraph responses, short narratives, first research assignments. Writers this age are still finding a voice, so the goal is rarely a formal case. The free tier covers a casual sanity-check on a single paper, and the sentence highlights give you something specific to ask about in a quiet check-in rather than a confrontation.
Five-paragraph essays, in-class writing, take-home assignments across several sections a day. 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 or a parent asks to see the evidence.
When a grade level or a department wants consistent handling across classrooms, the Business tier shares scan history across a workspace and produces the same clean one-page PDF every teacher can hand to an administrator. That consistency matters more in a school than in a research department, because the audience for the report is usually a parent or a principal, not a hearing board.
Whether or not your school runs Turnitin, TextSight is the same-class scan you run while you grade, so you have sentence-level context in hand rather than a vague feeling that something is off. It is a working tool, not a replacement for any official school check.
Bulk-upload the class set of PDF or DOCX submissions. TextSight returns a class dashboard in a few minutes with an Authenticity Score per essay, so the one or two that need a closer read rise to the top instead of getting lost in the stack. Pro and above.
You see the Authenticity Score next to the student name. A high score means grade normally. A middling score means glance at the sentence highlights and factor them into your read of a student you already know. A low score means open the full report and slow down before you write anything in the gradebook.
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. That is the format an administrator or a parent can actually follow, rather than a screenshot of a percentage.
Compared with reading every essay cold, quietly suspecting AI in a few, and raising it with no evidence to point to, the scan-then-grade pattern gives you something specific to talk about and spares everyone the worst version of the conversation.
Department licensing through Business. Full details on the pricing page.
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Yearly billing saves 25%. Department and school licensing available on Business. View full pricing →
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 and for younger students who write in short, even sentences before their voice fully develops. Your knowledge of the student is the final layer, not the classifier.
Failing a student or marking a permanent record on a single detector percentage produces bad outcomes, and it is hard to defend to a parent or a principal. The fair path is conversation first, evidence second, decision third, with the scan as supporting context throughout and a young writer always given the benefit of the doubt.
The sentence-level highlights are the lever. Ask the student to walk you through how they wrote a specific flagged paragraph, or to show an earlier draft from their notebook or shared doc. The flags give the student something concrete to respond to, which is far kinder to a 13 or 16-year-old than a vague accusation built on a number.
Rewritten AI text is hard for any detector to catch reliably. The durable defences are the ones you already control in a classroom: in-class drafting, multi-stage assignments with check-ins, and prompts that ask for personal experience or something specific to your lessons. The detector is one signal in that mix, not the whole answer.
How TextSight compares with other classroom detectors, and what to look for when picking one.
See the comparison →The higher-ed faculty workflow for graduate seminars, dissertation chapters, and journal pre-submission.
Faculty workflow →Plain-language explainer on perplexity, burstiness, and why false positives happen. Worth a read before any conversation.
Read the explainer →Free, Starter, Pro, Business. Yearly billing saves 25%.
See pricing →Free to try. No card.
How TextSight fits other teams and workflows.