Calibration is how you teach the assistant to grade like you before it grades a whole stack. It is optional, and it is one of the best ways to make the feedback feel like yours.
Add papers you have already graded
In the Submissions step, open the calibration set and add a few papers you have graded yourself:
- Upload graded PDFs, and the grade is read right off each paper.
- Import graded papers from Google Drive, if your papers live there rather than on this device. Connect Drive first in Settings, then browse straight to them.
- Pick from a past course on any LMS you have connected, and the grade comes across with the paper.
Each one lands under your calibration submissions with your grade attached. These are never on the roster and never released to students.
Picking from a past course closes the picker right away and brings the papers in behind the scenes, so you are not stuck waiting on a dialog. The calibration submissions bar shows how many have arrived while they land, and tells you when the import is done.
You can also cap a paper from this assignment into the set, using the cap button on its row. If that paper came in from Canvas already graded, its Canvas grade comes with it, so the set knows your score without you regrading.
When the grade is not on the paper
The set can only show you how the assistant compares to you on a paper it knows your score for. When a paper has no score the app can read, its Your score cell offers a score button: type the grade it earned, out of 100 or as a fraction like 34/40, and press Enter. Use it for a paper you graded off-platform, or to correct a grade that came across wrong. Clearing the box forgets the grade again.
Once you grade a paper here yourself, your grade replaces the one it arrived with.
Run them, and let it learn
Run the calibration papers through grading. For each one, the assistant compares its own proposal to the grade you gave, and learns from the gap. It surfaces the patterns it finds, for example "you score Evidence lower than the model does," as proposed calibration rules.
Open any calibration paper the moment it finishes and you see everything the assistant did: its score on every criterion, its findings, and its margin observations, exactly as on a roster paper. Nothing is held back until you confirm. What it learns comes from what you change: the observations you dismiss, the reason you give when you remove one, and the criteria where your number differs from its own.
Both score columns read as percentages, so the assistant's number and yours are always on the same scale. That matters most for a paper from a past course, which was graded out of a different total than this rubric. Hover either score to see the points behind it.
While a paper is being graded again, its row says Re-checking and dims the score. The number you see is from the last run and will update on its own when the new one lands.
Sometimes the assistant cannot read part of a rubric on a given paper. When that happens its score is marked partial, and hovering tells you what it was scored out of and which criteria it had to leave to you. The comparison to your grade, and everything it learns, is made on the points it could actually see.
Approve what is right
You review those proposed rules and approve the ones that match how you actually grade. Approved rules are injected into grading from then on, so the rest of the class lands closer to your standard. Nothing is applied until you approve it.
The trust bar
Before bulk grading unlocks, you confirm a small number of papers yourself. This is the trust bar: it makes sure the assistant has seen enough of your judgment before it grades at scale. Once you have crossed it, grading a whole class at once opens up.