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Education · Automated grading

The marking backlog is a retention problem hiding as an admin one

Banao builds automated grading that scores short-answer, code, and multiple-choice submissions the moment a student submits — with a confidence score on every call and a human review queue for anything borderline.

The system works inside your existing LMS. Feedback reaches the student the same day; faculty spend their review time on the calls the model flagged, not the full stack. Work that arrived at midnight is marked by morning.

Studylab AI— short-answer grading built into the practice platform with per-rubric written feedback.

What a Banao grading deployment includes

A grading system is only as useful as its integration. We build the model, the LMS wiring, and the faculty workflow — handed over as running software, not a specification.

Short-answer and essay marking against a rubric

The model scores each response criterion by criterion against a rubric you own — and returns a written comment per criterion, not just a number. Faculty see what the model said and can override in one click.

Code assessment with test execution

Submissions run against your test suite; the model also checks style, structure, and common error patterns against the assignment brief. Feedback names the specific failure, not just a score.

Multiple-choice and structured-response batch grading

Batch uploads processed in seconds, not hours. Submission formats handled include LMS exports, PDF scans, and direct API intake from your assessment tool.

Confidence scoring and borderline routing

Every graded submission carries a confidence score. Anything below your threshold goes into a faculty review queue before the grade is released — so the model never awards a mark it is uncertain about without a human in the chain.

Written feedback generation per rubric criterion

Students receive criterion-level feedback the same day they submit, not two weeks later when the assignment has left working memory. The comments reference the student's actual response, not generic phrases.

Gradebook and LMS integration

Grades and feedback push directly to your LMS gradebook — Canvas, Moodle, Blackboard, or a custom SIS — so there is no CSV import step and no double-entry for faculty.

Where automated grading is already marking

Metrics shown dotted (··) are being finalised in our case-study metrics pack. The deployments are live; we will not publish a number before it is verified.

Studylab AI

Short-answer grading built into a live practice platform

  • ··%reduction in manual marking time
  • ··hrsaverage feedback turnaround
  • ··%student satisfaction with feedback speed

Studylab AI needed grading that could score short answers on the fly — not just tick a multiple-choice answer. Banao built a model that grades against a stored rubric, returns written feedback in the student's language, and routes any answer the model scores with low confidence to a subject reviewer before the grade is shown to the learner.

We built our own grading system before we built yours

InterviewGod — Banao's own AI hiring tool — grades written and spoken responses from engineering candidates every week. The same scoring logic, confidence thresholds, and human-review routing that we trust with our own hiring decisions is the pattern we carry into institution deployments.

We are not explaining AI grading from a whitepaper. It decides who joins our ~300-person team. That is the accountability standard we hold ourselves to before we hold it to a build for you.

  • InterviewGodGrades written and spoken responses from every Banao engineering candidate — the same machinery we build for education clients.
  • VikaasRuns Banao's own demand-gen pipeline end to end.

When automated grading is the wrong fit

We would rather tell you the edge cases before you spend on a build. There are three situations where we will say to wait or scope carefully:

  • High-stakes finals and qualifying exams: for anything that determines a qualification or professional licence, a human examiner stays the decision-maker. AI can draft and flag; it does not award the final mark.
  • Highly creative or open-ended work: poetry, clinical case reflections, and open-ended research essays resist rubric grading. The model will score them; it will not score them well. We will say so in the Discovery Sprint.
  • No rubric exists: a grading model needs a rubric to align to. If your institution has not agreed what a criterion-level mark means, the first week is rubric design — not model training.

How we start — grading model in two weeks, not six months

We don't scope a grading system from a distance. We mark a sample of your real submissions first.

  1. AI Discovery Sprint2 weeks · fixed price

    We grade a sample of your real submissions against your rubric, measure accuracy against your faculty's marks, and hand back a report: what the model can grade reliably, where it needs a human, and whether the ROI maths work for your volume. Yours to keep regardless. Proceed, and the Sprint fee is credited against the build.

  2. Build

    Rubric alignment, model training on your submissions, and LMS integration — Canvas, Moodle, Blackboard, or custom SIS. The confidence threshold, the review queue, and the feedback format are yours to configure.

  3. Production & continuous improvement

    Faculty overrides feed back into the model each term. A marking dashboard shows grade distributions, model confidence, and override rates — so you can see where the model is drifting before it affects a cohort.

Frequently asked questions

For multiple-choice and structured short answers, accuracy against a verified rubric typically exceeds 90%. For open-ended short answers, accuracy depends on rubric clarity — the Discovery Sprint benchmarks your specific rubric and submission type before you commit to a build.

Yes, and the appeal workflow is part of the deliverable. Every AI-awarded grade carries a confidence score and a criterion-level explanation. Appeals route to faculty with both the student's response and the model's reasoning, so the review is faster than starting from scratch.

Canvas, Moodle, Blackboard, and Brightspace via LTI and API. For custom or legacy SIS installs, the integration audit in week one establishes the connector approach — we have handled on-prem installs and CSV-based gradebooks where API access is not available.

Submissions the model scores below your confidence threshold go into a faculty review queue before any grade is released. The model shows its reasoning so the reviewer sees what it was uncertain about. The threshold is yours to set — conservative or permissive, depending on the stakes of the assignment.

Submission data stays within your tenancy — we can deploy on your own infrastructure or a contracted cloud region. Student identifiers are decoupled from the training pipeline, and the model is trained only on your institution's data, not a shared dataset. We document every data flow and work to your existing DPA.

Send us a sample assignment — we will grade it against your rubric

Bring a set of real submissions and your marking rubric. In the first two weeks we will benchmark accuracy against your faculty's marks and tell you whether automated grading earns its keep for your course volume.

Book a 45-min scoping call