AI-assisted assessment grading with human override
Answer sheets are digitized, scored against the marking scheme and passed to a reviewer. Nothing counts as a result until a person has looked at it and finalized it.
The challenge
Grading written answers by hand takes days. Handwritten sheets are never digitized, scoring varies from one evaluator to the next, and the institution has little data on how a class or cohort actually performed on each question.
Replacing evaluators with a model brings its own risk. A score nobody can explain or correct will not survive a student appeal. The goal is faster grading in which a person still owns every final mark.
What it does
Digitization of sheets
Handwritten and typed answer sheets are read by OCR and layout extraction, so answers become structured text matched to the right question.
Scoring against the scheme
A model scores each answer against the question paper and the marking scheme, with marks, a status and written feedback for every question.
Consistent rules
The same paper, scheme and grading rules apply to every sheet in a batch, which reduces drift between evaluators and sessions.
Side-by-side review
A reviewer sees the original sheet and the model's results together, and can compare each score with what the student wrote.
Human override
Reviewers change any mark and add a comment. Unreadable or doubtful answers are flagged for a person instead of being scored on a guess.
Class and cohort insight
Question-level results support analysis of how learners performed, so teaching teams can see where a topic needs more attention.
How it works
Set up the assessment
Provide the question paper and the marking scheme or model answers, and choose the model that your policy allows for grading.
Digitize and score
Answer sheets are submitted as a batch, read by OCR and scored question by question, with feedback that names what was right and what was missing.
Review and override
A reviewer opens each sheet beside the model's results, adjusts marks where needed, and resolves anything flagged as unclear.
Finalize and report
Finalizing locks the sheet against further edits. Results are exported and used for feedback to learners and analysis by question.
Under the hood
- Document intake
- Images
- Handwriting recognition
- Layout extraction
- Grading
- Any chosen model
- Structured JSON output
- Rubric-driven prompts
- Review
- Side-by-side viewer
- Override with comment
- Locked final results
- Output
- CSV export
- JSON results
- Cloud object storage
Inside the harness
Observability
Batch progress, flagged answers and reviewer changes are visible to the assessment team, so they see where the model needed correction and where grading is stuck.
Evaluation
Model scores are treated as proposals. Reviewers compare them with the original sheet, override with a comment, and only then finalize.
Guardrails
Grading rules fix the output format, limit partial credit by the marks at stake, and send unreadable answers to a person.
Audit
The model's result, each reviewer change and the finalization are recorded, so any mark can be explained if a student appeals.
Business outcome
Grading finished sooner. Evaluators start from a scored, explained draft for every sheet, so turnaround moves from days toward the same day and their time goes to judgement.
More consistent marking. One scheme and one set of rules applied to every sheet means the same answer is scored the same way, whoever reviews it.
Final say stays with people. Every mark can be overridden and nothing counts until finalized, which keeps accountability with the institution and not the model.
Data for teaching. Digitized, question-level results give teachers a view of performance trends that hand grading never produced.
Where it is used
- A school board digitizes handwritten term papers and has teachers review proposed scores.
- A university department grades short-answer and essay questions across a large cohort with one scheme.
- A training provider returns question-level feedback to learners after a written assessment.
- A certification body routes unclear or doubtful answers to senior reviewers while routine sheets move on.
Frequently asked questions
Who decides the final mark?
A person does. The model proposes marks and feedback, a reviewer can override any of them with a comment, and a sheet is locked only when the reviewer finalizes it.
What happens when handwriting is hard to read?
Answers that cannot be read reliably are flagged for human review rather than scored. Quality of the scan matters, so a scan quality check before grading helps.
Can different languages be graded?
Multi-language grading appears in the case study that this use case draws on, but support depends on the model and OCR you choose. Test with your own languages before relying on it.
How does this relate to the Academic paper correction product?
That product is the evaluation module itself. This page describes the grading workflow around it: digitize, score, review, override and finalize, with the controls that keep a person accountable.
See it on your own documents and data
Tell us what you want to automate and we will show you how it runs inside the harness.