SIMAD AI Examination Moderation Platform

AI-assisted academic quality assurance platform that evaluates examination documents against formal moderation criteria while preserving deterministic evidence verification and human committee authority.

~1,100Course/exam contexts

OpenAILLMDocument AINestJSNext.jsPostgreSQLHuman-in-the-Loop

Engineering Decisions

Architected and independently developed the system end-to-end for SIMAD University's examination moderation workflow.

Designed structured AI evaluation using examination documents, course context, formal moderation criteria, and schema-validated LLM outputs.

Implemented deterministic verification of AI-proposed evidence, provenance tracking, prompt-injection warnings, reliability classification, and fail-closed behavior.

Preserved human committee authority by separating AI-generated evidence from final PASS / REVISE / REJECT decisions.

Designed the platform for approximately 1,100 course and examination contexts across university faculties.

Architecture

Exam DocumentsExtractionCourse ContextOpenAIStructured EvidenceVerification LayerCommittee Review

Data & AI Flow

Exam documents and academic context are assembled for LLM evaluation, converted into structured evidence, deterministically verified against source material, then presented to committee members for final academic judgment.

Engineering Tradeoff

The system deliberately treats model output as untrusted evidence rather than allowing the LLM to make academic decisions directly.

Proof Points

ArchitectureData FlowFailure HandlingScaleConstraint