Accelerator 17 · Education
Rubric grading and feedback assistant
Grades written answers against the teacher's rubric with evidence for every score, writes feedback a student can act on, and leaves the final mark to the teacher.
The problem
- Teachers spend a large share of their week marking, which is the main reason feedback reaches students late or thin.
- Marking drifts between teachers and over a long pile of scripts, which is hard to defend when grades are challenged.
- Students get a number, not what to do next.
What it does
- Applies the teacher's own rubric criterion by criterion, quoting the part of the answer behind each level.
- Writes two strengths and two next steps in plain language for the student.
- Flags answers it is unsure about, possible copying, and off-topic responses for the teacher.
- Suggests a level only. The teacher confirms or changes every mark; the class view shows common misconceptions.
Paste a question, a rubric and a student answer and get a criterion-by-criterion assessment and feedback.
Live demo: Rubric grading and feedback assistant
Synthetic data only · nothing you type is stored
Output
Criterion-by-criterion levels with evidence, a suggested overall level, and feedback for the student.
The demo first calls the Surge in-house AI server and falls back to a hosted open-source model if it is busy. The badge on the output shows which one answered. Limited to a few runs per visitor per day.
How it integrates
In your deployment the accelerator runs inside your environment, behind your single sign-on, and is connected to the systems below. You choose the hosting and the model.
Systems and APIs
- LMS: Moodle, Canvas, Google Classroom, Microsoft Teams for Education (LTI 1.3)
- Scanned scripts via OCR / handwriting recognition
Data we need for a pilot
- Rubrics and model answers for the assignments in scope
- A set of teacher-marked scripts to measure agreement before go-live
Where it runs
- Institution's private cloud or on-prem; student work never trains a model
Open-source models we start with
- Qwen3-class 14B instruct model
- Handwriting OCR (TrOCR-class) for paper scripts
Walkthrough
A short recorded walkthrough of the rubric grading and feedback assistant: the input, the output and how a reviewer uses it.
Next accelerator: Multilingual citizen services assistant →Walkthrough video coming soon
We are recording a short walkthrough of this accelerator. Meanwhile, run the live demo above or book a working session.
Run the education accelerator on your data
In a working session we map it to your process, systems and rules, and agree what a pilot must prove before it goes live.