PLAYBOOK

    Is your LMS AI-ready? A 6-point evaluation checklist

    Before you add AI grading, check whether your platform can actually carry reliable, auditable evaluation. Six things to verify.

    Time to read: 6 min · For: LMS / EdTech product teams

    CrazyGoldFish

    IN SHORT

    An LMS is ready for AI evaluation when it captures answers as structured data, can call an evaluation API and ingest results back, gives teachers review-and-override with an audit trail, handles handwritten and subjective answers (not just MCQs), keeps every score traceable, and works in low-bandwidth settings. This checklist walks through all six.

    // WHAT.YOU'LL.CHECK

    01Structured assessment data
    02API-first architecture
    03Human-in-the-loop hooks
    04Handwritten & subjective input
    05Governance & auditability
    06Offline & low-bandwidth resilience
    1

    Structured assessment data

    CHECKDoes your platform store answers in a structured, retrievable form, not just a final score?

    WHYEvaluation infrastructure needs the answer itself to read, score, and explain — a discarded answer can't be re-evaluated or audited.

    2

    API-first architecture

    CHECKCan your platform call an external evaluation endpoint (REST) and ingest structured results — scores, per-question feedback, flags — back into your gradebook?

    WHYThe evaluation layer should plug in, not require a rebuild.

    3

    Human-in-the-loop hooks

    CHECKCan a teacher review, override, and edit an AI score, with every change captured as an audit trail entry?

    WHYIn any consequential assessment the teacher must stay the final authority, and the trail is what makes the result defensible.

    4

    Handwritten & subjective input

    CHECKCan it handle scanned handwritten scripts (OCR) and subjective answers, or only objective/MCQ?

    WHYThe hard, high-value evaluation work is subjective — that's where time is lost and where reliable AI help matters most.

    5

    Governance & auditability

    CHECKCan you trace every score to its inputs, flag and route outliers, and produce an audit pack?

    WHYAt scale, and especially in government or regulated contexts, auditability matters as much as accuracy.

    6

    Offline & low-bandwidth resilience

    CHECKDoes the workflow degrade gracefully on Tier-2/Tier-3 connectivity?

    WHYA tool that assumes bandwidth isn't a tool most of India's classrooms can rely on.

    KEEP READING

    Keep reading

    // COMMON.QUESTIONS

    Common questions

    Do we need to replace our LMS to add AI evaluation?

    No. If your platform can call a REST API and ingest structured results, the evaluation layer plugs in alongside what you have.

    Can teachers still control the final score?

    Yes — teacher review and override with a full audit trail is the correct default for any consequential assessment, and it's how CrazyGoldFish is built.

    Does this work for handwritten answers?

    Yes. Handwritten and subjective answers are the core case — OCR plus subjective evaluation, not just multiple-choice.

    See how the evaluation layer fits your platform