RootsAI

Overview

When a student struggles with a topic in Class 9, the real problem is often something from Class 7 or 8 that was never fully understood. Most learning tools don't go looking for that. RootsAI does.

The system takes a student's problem, maps it onto an authored curriculum graph, traverses prerequisites to build a "Concept MRI," diagnoses knowledge gaps through targeted quizzes, and guides the student through a gamified remediation path — ending with a Feynman-style verification where the student teaches the concept back to the AI.

Core principles:

The graph is the source of truth, not the LLM. Every AI output is validated against the real curriculum graph. The LLM maps problems to existing nodes — it never invents curriculum.
Deterministic where it matters. Same problem → same concept map → same prerequisite tree → same quiz. Reproducible diagnostics, every time.
Mastery is earned, not assumed. Students prove understanding by re-answering missed questions and explaining concepts back (Feynman technique).

How It Works

Student submits problem (text or photo)
        │
        ▼
   ┌─────────┐    Zod-validated against
   │   Map   │───▶ real graph slugs
   └────┬────┘    (hallucinated slugs rejected)
        │
        ▼
  ┌──────────┐    Deterministic BFS
  │ Traverse │───▶ prerequisite subtree
  └────┬─────┘    ("Concept MRI")
       │
       ▼
   ┌────────┐     Authored questions verbatim,
   │  Quiz  │────▶ or generate-once + cache
   └────┬───┘     in Firestore
        │
        ▼
  ┌──────────┐    Duolingo-style serpentine path,
  │  Learn   │───▶ foundational-first ordering,
  └────┬─────┘    sequential unlock
       │
       ▼
  ┌──────────┐    Student teaches concept back
  │  Verify  │───▶ to the AI (Feynman technique)
  └──────────┘

Two modes

Legacy (demo): Hardcoded NCTB Class 9 Physics graph — mechanics vertical with 19 authored nodes and hand-written quiz questions.
Classroom: Teachers upload materials → AI extracts a concept graph → students run diagnostics against that graph. Works with any subject.

Features

Diagnostic Engine

GraphRAG mapping: LLM routes problems to existing curriculum nodes with Zod hard-validation — no hallucinated concepts.
Concept MRI: Deterministic BFS traversal builds the same prerequisite tree for the same root, every time.
Hybrid quizzes: Authored questions used verbatim; unauthored nodes get AI-generated questions cached in Firestore.
Adaptive remediation: All gaps recorded and ordered foundational-first. Lesson plans are deterministically structured from the graph.

Learning Path

Serpentine path: Duolingo-style winding path of medallion nodes with strict sequential unlock.
Earned mastery: Each gap node reuses the exact MCQ the student missed (options shuffled). Answer correctly → mastered.
Feynman verification: Students teach the concept back to the AI via voice or text+photo before moving on.
Gamification: Progress bar, XP system (+10/mastered), celebration animations (reduced-motion guarded).

Classroom System

Teacher workflows: Create classrooms, upload materials (PDF/syllabus/notes/textbook), manage students.
Dynamic graph extraction: AI extracts concept dependency graphs from uploaded materials for any subject.
RAG-powered tutoring: LangGraph agent retrieves from teacher's knowledge base — never fabricates content.
Analytics: Teacher dashboard with student progress and engagement tracking.

Additional

Multimodal input: Students can photograph problems (camera/upload) — image is sent alongside text to the diagnostic engine.
Voice transcription: Audio input transcribed via Gemini for Feynman verification.
Real-time chat: Contextual AI tutor grounded in classroom materials with citation support.
Role-based access: Firebase Auth with student/teacher roles and onboarding flow.

Architecture

Layer	Stack	Responsibilities
Frontend	Next.js 16, React 18, Chakra UI, Tailwind	Role-based app shell, diagnostic flow, serpentine learning path
Diagnostic Engine	Custom pipeline (map → traverse → quiz)	Problem mapping, prerequisite traversal, quiz resolution
Tutor Agent	LangGraph + LangChain	Stateful multi-tool agent: RAG retrieval, mastery check, grounded response
Curriculum Graph	Authored graph + Neo4j (optional)	Source-of-truth concept dependencies, BFS traversal
RAG Pipeline	LangChain TextSplitters + Gemini Embeddings	Chunk materials, embed, store in Supabase pgvector, HNSW cosine search
LLM Provider	Gemini (primary) + fallback chain	Structured JSON generation with retry + model-chain fallback
Auth / Data	Firebase Auth + Firestore	Identity, roles, diagnoses, classrooms, quiz cache, progress
File Storage	Supabase Storage	Problem photos, Feynman verification uploads

The Infinity AI BuildFest

Built for The Infinity AI BuildFest organized by CloudCamp Bangladesh. The idea: when a student struggles with a Class 9 topic, the real gap is often something from Class 7 or 8 that was never fully understood. RootsAI goes looking for that root cause.

Team

@bokaif (Badruddoza Kaif)
@MrDuck04 (Abror Mahir)
@mdnaimur0 (Md. Naimur Rahman)
@Moraladnan (Md. Adnan Arefin Ratul)