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Rubric-based AI assisted grading

Grading is where the teacher's time goes and also where quality degrades: the feedback on submission forty doesn't resemble the one on submission three, and the student who handed in last gets less. That's a fatigue problem, not a judgement one.

The most common implementation mistake is asking the model for the final grade. That shifts responsibility to a system that can't carry it, and it wastes what AI actually does well: read everything, apply the same criterion across all forty submissions, and point to where the evidence sits in the text. The grade stays the teacher's.

Short answer

AI assisted grading produces a draft: a tentative grade per rubric criterion and feedback quoting the passage of the work that justifies it. The teacher reviews, adjusts and signs. The grade that reaches the record is always the teacher's, and the system logs how much they had to change it.

How it works

01

The rubric is made explicit

Criteria, levels, and what evidence maps to each level, written down. If the rubric lives in the teacher's head, the project starts by writing it — and that work already improves human grading.

02

It's assessed criterion by criterion, not all at once

One pass per criterion, each with its evidence. Asking for the overall grade in one go produces a plausible number and vague feedback — precisely what's useless.

03

The teacher reviews in a diff-style interface

They see the draft, the quoted evidence, and can change a criterion's grade in one click. Reviewing has to be faster than grading from scratch, or the teacher stops using it.

04

The gap between draft and final grade is measured

Per criterion and per teacher. It's the metric that tells you whether the system is calibrated, and the one that decides whether to extend to another subject or revisit the rubric.

What gets measured

  • Minutes per graded submission, before and after.
  • Mean gap between suggested and final grade, per criterion.
  • Consistency: the same submission graded twice — same grade?
  • Student appeals about the feedback received.

What's needed on your side

  • Written, stable rubrics. Without a rubric there's no assisted grading — there's a generated opinion.
  • A set of teacher-graded submissions, to calibrate and measure the gap.
  • A review interface inside the flow the teacher already uses, not in another tool.
  • A clear policy, communicated to students, that AI assists in grading.

When it isn't worth it

  • If assessment is multiple choice, it's already automated and needs no model. This is for written work, code, cases and projects.
  • If every teacher grades by a different standard and the institution doesn't want to unify it, the system will expose that inconsistency and the conflict is political, not technical.
  • If the assessment is high-stakes — admissions, official certification, a degree — an automatic draft adds reputational risk without saving enough. There, grading stays double and human.

Related questions

Does the student know AI graded them?
They know AI assists and that a teacher signs the grade, because it's stated in the course policy. Hiding it is untenable: it surfaces at the first appeal, and in several jurisdictions automated decisions about a person carry transparency and human-review obligations.
How much time does it really save?
It depends on how long review takes, not how long the model takes. If the interface still forces the teacher to read the whole submission, the saving is near zero. The saving appears when the quoted evidence lets them verify the criterion without rereading everything — and that's a product decision more than a model one.
Does it detect whether the work was written by AI?
We don't, because current detectors have a false-positive rate that isn't acceptable when the consequence is an academic dishonesty accusation. What can be done is redesigning the prompt to require process, iteration and personal context — which is what a model can't fabricate.

Use cases in this industry

Semantic search over learning content

How to implement semantic search in a learning platform: over transcribed video and your own material, citing the timestamp and respecting the student's scope.

Next step

What education product should exist next?

Tell us what you are building, what is not working yet or which AI opportunity you want to evaluate. In the first conversation, we will tell you where we would start, what it requires and what we would not build.

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