Play 75
Exam Generation Engine
Medium✅ Ready
Auto-generate exams with difficulty calibration, rubric generation, and anti-cheating variation.
Automated exam generation system that creates assessments from curriculum materials with precise difficulty calibration. Azure OpenAI generates questions across Bloom's taxonomy levels, produces detailed rubrics and answer keys, and creates anti-cheating question variations. Blob Storage manages curriculum documents and exam banks, Cosmos DB stores question metadata and difficulty analytics, and Functions orchestrate the generation pipeline with batch processing support.
Architecture Pattern
Curriculum → question generation → difficulty calibration → rubric + answer key → anti-cheat variations
Azure Services
Azure OpenAIAzure Blob StorageAzure Cosmos DBAzure Functions
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Exam Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (203 lines), evaluate (129 lines), tune (245 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with OpenAI key input + envFile
TuneKit (AI Config)
- config/openai.json — question generation and rubric prompts
- config/exam.json — difficulty distribution, question types, Bloom's levels
- config/guardrails.json — fairness, bias detection, content appropriateness
- evaluation/eval.py — Difficulty accuracy >85%, Rubric coverage >90%
Tuning Parameters
Difficulty distribution curveBloom's level targetingQuestion type ratiosAnti-cheat variation countRubric granularity
Estimated Cost
Dev/Test
$40–100/mo
Production
$500–2K/mo