Play 74
AI Tutoring Agent
1-on-1 personalized AI tutoring with Socratic method and adaptive difficulty.
Personalized AI tutoring system using Socratic questioning to guide students through concepts rather than giving answers directly. Azure OpenAI powers adaptive conversations that detect knowledge gaps and adjust difficulty in real time. Cosmos DB tracks per-student progress across subjects, AI Search retrieves curriculum-aligned materials, and Static Web Apps delivers the interactive learning interface. Functions handle session orchestration and progress analytics.
Architecture Pattern
Socratic agent: knowledge gap detection → adaptive questioning → difficulty calibration → progress tracking
Azure Services
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Tutor Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (179 lines), evaluate (123 lines), tune (229 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 — Socratic questioning and explanation prompts
- config/tutoring.json — difficulty levels, subject taxonomy, gap thresholds
- config/guardrails.json — age-appropriate content, bias prevention
- evaluation/eval.py — Learning gain >15%, Engagement >80%
Tuning Parameters
Machine evidence
FrootAI evidence lifecycle
This is an internal evidence maturity label, not third-party certification, accreditation, legal compliance, or a production guarantee. Missing or expired evidence demotes automatically; catalog claims cannot promote a play.
This play currently has design evidence only. A runnable scenario, endpoint evaluation, and build receipts are the next contiguous gates.
Repo Intelligence
v1A no-clone, revision-pinned map for agents and humans. Observed evidence is separated from inferred flow so the output stays useful without pretending to be a full call graph.