PLAYBOOK
How a school cluster or district adopts AI-assisted evaluation of handwritten answers safely — teachers in control, every score auditable.
CrazyGoldFish
IN SHORT
A government deployment of AI evaluation succeeds when it is governed, not automated. You start with a controlled pilot graded in parallel by teachers, adopt a three-stage workflow (production, audit, adjudication) with the teacher as final authority, set a policy annex for review windows and escalation, govern the small tail of hard cases openly, and only then scale in a hybrid mode. This is the blueprint we validated in a government Model School in Srikakulam (see the case study), expressed as steps a district can follow.
// WHAT.YOU'LL.DO
CHECKPick a small, real set of scripts and grade them in parallel with both teachers and the AI.
WHYYour reliability bar before scaling. A controlled set is what produces trustworthy evidence; a big-bang rollout produces neither evidence nor trust.
CHECKRun every script through production scoring, an audit layer that flags quality and outlier issues, and adjudication for disagreements against the marking scheme.
WHYNothing here is optional — the teacher remains the final authority and the AI is the governed layer underneath.
CHECKFreeze the operating rules — a provisional-scores window, a query SLA, and an escalation timeline.
WHYYour specific windows (e.g. a 72-hour provisional window, a five-working-day query SLA, escalation within two working days). Frozen rules are what make a public deployment defensible.
CHECKGive teachers review, override, and edit on every score, and log every action.
WHYYour approval states from upload to final publication. Captured overrides become improvement signals, not lost edits.
CHECKRoute the small set of high-severity or low-confidence cases to teachers via an upload quality gate, hotspot routing, and outlier review, under a defined escalation ladder (teacher → principal → block/district QA), with a monthly audit pack.
WHYYour thresholds. Reporting the tail is the design point, not a footnote to hide.
CHECKWiden from the pilot to more subjects, schools, and assessments, keeping the workflow and audit pack on throughout.
WHYYour go-live and expansion gates. The teacher stays in the loop at every consequential step as you scale.
// WORKFLOW
// CHECKLIST
KEEP READING
// COMMON.QUESTIONS
Does AI replace teachers in this model?
No. The teacher is the final authority on every score. The AI is a governed decision-support layer; overrides and edits stay active and are captured as improvement signals.
Is this proven in a real government school?
Yes. It was validated at a government Model School in Srikakulam, Andhra Pradesh, across two phases. See the APMS case study for the full reliability and governance evidence.
How is it kept accountable?
Every score is traceable, the hard-case tail is reported and governed through flagging and a monthly audit pack, and a defined escalation ladder runs from teacher to principal to district quality assurance.
Does it work in low-bandwidth classrooms?
Yes. The workflow is validated for offline and low-bandwidth use, because a tool that assumes connectivity isn't one most government classrooms can rely on.