PLAYBOOK
How an LMS or assessment platform adds reliable, auditable AI evaluation as an API layer — without rebuilding your stack.
CrazyGoldFish
IN SHORT
Adding AI evaluation to an LMS is an integration, not a rebuild. You send the question paper, marking scheme, and student answers to the evaluation API; you get back structured scores, per-question feedback, and quality flags; you map those into your gradebook; and you wire a teacher review-and-override step with an audit trail. A typical first integration takes about a day. This playbook walks through the six steps and the decisions at each one.
// WHAT.YOU'LL.DO
CHECKRequest API credentials and confirm the auth method.
WHYWhich environment (sandbox first, then production). Sandbox lets you validate the full flow on sample scripts before touching live data.
CHECKPOST the three inputs — question paper, marking scheme, and the student answers (typed or scanned handwritten scripts).
WHYReal-time per-submission vs batch per-assessment. Batch is simpler to start; real-time is better for instant feedback.
CHECKIngest the response — per-question scores, totals, natural-language feedback, and quality flags — and map it into your gradebook schema.
WHYWhich fields you store now vs later (store the feedback and flags, not just the score — that is the part you cannot regenerate).
CHECKSurface AI scores to the teacher with edit and override controls, and log every change as an audit-trail entry.
WHYYour approval states (e.g. provisional → reviewed → published). The teacher stays the final authority; the AI is the governed layer underneath.
CHECKUse the quality flags (OCR issues, low-confidence items, outlier scores) to route those specific answers to a teacher instead of auto-publishing.
WHYYour thresholds. A small, well-routed flagged set is what makes the rest trustworthy.
CHECKRun one real cohort end-to-end, compare a sample against teacher marks, then widen.
WHYYour go-live gate (e.g. reliability and turnaround targets). Keep the audit trail on from day one so the deployment is defensible.
// ILLUSTRATIVE.SHAPE
Illustrative shape. See the API docs at /docs for the exact endpoints and schema.
// CHECKLIST
KEEP READING
// COMMON.QUESTIONS
Do we have to replace our LMS?
No. AI evaluation plugs in as an API layer. If your platform can make a REST call and store structured results, you keep your existing stack.
How long does a first integration take?
A typical first integration runs about a day: authenticate, send inputs, map results, and wire the teacher review step. Edge-case routing and scaling come after the pilot.
Can teachers still control the final score?
Yes. Teacher review, edit, and override with a full audit trail is the correct default, and overrides are captured as improvement signals.
Does it handle handwritten answers?
Yes. Scanned handwritten scripts are supported via OCR; low-confidence extractions are flagged and routed to a teacher rather than auto-scored.