A one-click AI detection and Plagiarism Risk check inside the Classroom grading view, writing results back as a private grader-side comment. The integration is in planning. Target ship window is Q4 2026, contingent on waitlist demand. This page is the honest status, the planned scope, and the way to influence what we build.
A lot of detector tools list LMS integrations they have not shipped. This page is the opposite of that. Here is exactly where the Google Classroom integration sits in our pipeline today.
In planning. We have a written product spec, a paper sketch of the grader-side comment format, and rough engineering estimates. We have not started the Google OAuth verification work, which on its own typically takes six to ten weeks of back-and-forth with Google's review team. No code has been written against the Classroom API yet.
Three things, in order. First, enough waitlist signal to justify the build over Canvas, Blackboard, Schoology, Moodle, and D2L Brightspace, all of which are also requested. Second, Google OAuth restricted-scope approval, since the Classroom courseworks scope is treated as sensitive. Third, FERPA and DPA review with at least one district pilot partner before we open it to general availability. Any of those slipping pushes the window into 2027.
Paste-and-scan workflow at app.textsight.ai, the Chrome extension on any page including a Classroom turn-in, and bulk file upload on the Pro and Business tiers for processing multiple submissions in one batch. Many teachers already run Classroom assignments through the bulk upload at the end of a grading session. It is not one-click, but it works today and the detector accuracy is identical to what the integration will use.
The scope below is the working plan, not a feature promise. Items marked Planned are in the v1 spec. Items marked Out of scope are deliberately excluded; we will not ship them in v1 even if asked.
| Capability | v1 status | Notes |
|---|---|---|
| Pull student turn-ins via Classroom API | Planned | Read-only access on the courseworks.students scope, teacher-authenticated only. |
| Run AI detector on each submission | Planned | Same detector and model as app.textsight.ai. No separate calibration. |
| Run Plagiarism Risk check | Planned | Bundled in the same pass, no extra teacher click. |
| Write result as private grader-side comment | Planned | Visible only to the teacher, not the student. Includes a TextSight report link. |
| One-click bulk scan for a whole assignment | Planned | Process all submissions for an assignment with one teacher action. |
| Per-assignment scan history in TextSight | Planned | Teacher-side log of what was scanned, scored, and when. |
| CSV export of class-level results | Planned | For teachers who keep a separate gradebook outside Classroom. |
| Sentence-level highlights in the report link | Already in detector | The web report already shows per-sentence colour-coded highlights. |
| Auto-apply grades based on AI score | Out of scope | By design. Detector signals inform a teacher conversation, not a grade. |
| Notify students of their AI score | Out of scope | Teachers told us this should be a private signal, not a public flag. |
| Auto-flag or auto-reject submissions | Out of scope | No automated discipline events. Ever. |
| Student-side appeal portal | Out of scope | Belongs with the school's existing academic-integrity process. |
| SSO with Google Workspace for Education | Planned via Google OAuth | Teacher signs in with the same Google account they use for Classroom. |
| Admin-level deployment across a district | Post-v1 | v1 is teacher-installable. District-wide deployment ships after pilot feedback. |
| Real-time scanning during typing | Out of scope | Scanning happens on turn-in, not during the student's writing session. |
This scope is set against TextSight's existing detector accuracy. Nothing about the integration changes the underlying model: same calibration, same per-sentence rationale, same FPR on ESL writing.
These are the recurring patterns from the educator interviews and waitlist replies. Nothing on this list is hypothetical; every bullet maps to a teacher who told us they needed it.
Teachers grading 90 to 150 Classroom turn-ins in one Friday afternoon do not have 20 extra seconds per submission to copy, switch tabs, paste, scan, switch back, and write a note. The integration's whole purpose is collapsing that loop to a single grader-side action so the AI signal is available without breaking grading flow.
When one essay scores high on AI, the immediate next question a teacher asks is whether it is a class-wide pattern. The integration's per-assignment view shows the score distribution across all submissions, so the teacher can tell at a glance whether to address one student or rebrief the whole class on AI use.
Classroom assignments often have co-teachers. The planned private comment is visible to all teachers on the course, so a co-teacher sees the same AI signal without a second scan. This is a small detail that comes up constantly in our interviews and is impossible to do cleanly with paste-and-scan.
Teachers consistently tell us they want a private nudge to bring up AI use in a one-on-one conversation, not an automated flag that triggers an integrity process. The grader-side comment is built for that conversation. No auto-grading, no auto-notification.
Department heads and curriculum directors want quarter-over-quarter AI-usage trend data without seeing any individual student. The post-v1 CSV export is sized for that ask: aggregated, anonymisable, exportable to whatever the district uses for end-of-term reporting.
FERPA is the school's compliance posture, not a vendor checkbox. TextSight's job is to be a clean upstream partner so the school can defend the deployment. Here is how that works for the planned Classroom integration.
By default, TextSight does not retain student turn-in content beyond the active scan session. The detector reads the text, returns a score and per-sentence highlights, and discards the content from working memory once the report is delivered. Scan metadata (date, score, document length) is kept on the teacher's TextSight account so the per-assignment view works. The student's actual essay text is not.
We sign a Data Processing Addendum on request for school districts and higher-education institutions. The template is GDPR-aligned and FERPA-compatible, names TextSight (Lacewing Technologies) as data processor, and specifies the retention posture above. Email support@textsight.ai with "DPA request" and the institution name to get the current draft.
The planned integration runs under the teacher's authenticated Google Workspace session via Google OAuth. There is no separate student data export pipeline, no shadow account creation, and no second authentication for students. The Classroom API access uses the courseworks.students scope, read-only.
TextSight's current subprocessor list (compute, storage, email) is published on the Trust Center page when it goes live. For the Classroom integration, the only added subprocessor is Google itself, since the OAuth and API calls are first-party Google services on the teacher's authenticated session.
Run the integration through your existing third-party application review (Common App Review for K-12 districts, or your institution's IT security review for higher ed) before deploying it more broadly. We will provide the architecture diagram, the DPA, and the subprocessor list. The "is this FERPA okay" answer always belongs to the school, not the vendor.
There is no signup API behind this yet. The honest version: email us with the details below and we will add you to the build-priority list. The clearer your workflow description, the more likely v1 fits it.
Email support@textsight.ai with the subject line "Google Classroom waitlist" and please include:
We read every reply. Waitlist volume directly determines build priority across Google Classroom, Canvas, Blackboard, Schoology, Moodle, and D2L Brightspace. No marketing email list, no auto-drip. You will hear from us when the build moves from planning to engineering, and again at private beta.
Prefer to talk first. Reply with a request for a 20-minute call and we will set one up. The product team runs the educator interviews directly, not a sales rep.
Every LMS integration on this list is in the same planning state as Google Classroom. The waitlist signal across all six is how we pick which one ships first.
Planned SpeedGrader integration. Read-only courseworks scope. Same v1 scope as Classroom.
See the roadmap ›Planned Grade Center integration via the Learn REST API. Higher-ed focused v1.
See the roadmap ›Planned local plugin for self-hosted Moodle instances. Open-source-friendly install.
See the roadmap ›The educator hub. Detection methodology, ESL fairness notes, and how teachers use TextSight today.
Open the hub ›K-12 and higher-ed workflow notes. How to read scores, what to do on a flag, how to talk to a student.
Read the guide ›The other big classroom integration request. Same planning-phase status, same waitlist process.
See the roadmap ›The higher-ed integration request. REST API, Grade Center, same planning-phase status.
See the roadmap ›The underlying detector that the integration will use. Calibration, ESL fairness notes, benchmark dataset.
Read the methodology ›Free, Starter $9.99, Pro $19.99 ($14.99 annual, $13.99 .edu), Business $39.99 ($29.99 annual).
See pricing ›Tell us how your classes use Classroom and we will build the v1 against your workflow. Until then, the web detector and Chrome extension cover the paste-and-scan path.