EduSmart AI Tutor

An AI-powered personalized learning platform exposed through a Model Context Protocol (MCP) server, so an MCP-compatible AI client (Claude, ChatGPT Apps SDK, or any MCP host) can tutor a student through a real learning-workflow platform — knowledge retrieval, assessment, mastery tracking, and personalized recommendations — not a generic chatbot with a system prompt.

Student → AI Tutor → Knowledge → Assessment → Mastery → Next Action → Demonstrated Competence

What this is

• A relational learner model (PostgreSQL/SQLite): students, courses, a skill prerequisite graph, mastery, attempts, assessments, projects, and an append-only learning history.

• A real RAG pipeline: document ingestion → chunking → embeddings → a FAISS vector index → a retriever that joins back to source metadata for attribution.

• A deterministic-first learning engine: mastery calculation, prerequisite resolution, and the get_next_learning_action recommendation are pure functions — no LLM call decides them.

• An LLM provider abstraction (stub / OpenAI / Anthropic) used only where language generation genuinely helps: explaining a concept, generating novel questions, and grading free-text conceptual answers.

• A LangGraph tutor workflow that routes a request through explicit nodes (classify intent → load student state → teach/practice/assessment/project → update learning state → recommend next step) instead of an unconstrained agent loop.

• A 17-tool MCP server (app/mcp/server.py) plus an equivalent REST API (app/main.py), both thin adapters over the same authorization-enforcing service layer.

Related MCP server: OLCP

Status

59 automated tests, all passing, across unit, integration, MCP, RAG, and evaluation suites — run python -m pytest tests/ -q to verify. See docs/DEVELOPMENT.md for a phase-by-phase IMPLEMENTED/TESTED/PARTIALLY-IMPLEMENTED breakdown (Docker was written but not executed in this build environment; everything else has been run for real, not just written).

Quick start

python -m venv .venv && source .venv/Scripts/activate   # or .venv/bin/activate
pip install -e ".[dev]"
cp .env.example .env
python -m alembic upgrade head
python scripts/seed_data.py
python scripts/ingest_documents.py
uvicorn app.main:app --reload &            # REST API on :8000
python scripts/run_mcp_server.py &         # MCP server on :8765
python -m pytest tests/ -q                 # 59 passed

Full setup, every command explained, and known pitfalls: docs/DEVELOPMENT.md.

Documentation

License

MIT — see LICENSE.