📝 SynapseEd - Next-Generation AI-Powered Learning Platform

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🌟 Overview

SynapseEd is a cutting-edge educational platform that revolutionizes learning through advanced AI technologies. It enables real-time collaboration, intelligent content generation, and highly personalized learning experiences to empower both students and educators.

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🛠️ Tech Stack

🧩 Frontend Architecture

Category	Technologies
Core Framework	Next.js 15.2.4 , TypeScript
UI/UX	Radix UI, TailwindCSS, Shadcn/ui, Framer Motion
Real-time Features	Socket.IO, Server-Sent Events (SSE), Server-Side Rendering (SSR)

🧪 Backend Architecture

Category	Technologies
API Framework	Flask, eventlet, NEXT.js
AI/ML Stack	LangChain, LangGraph, OpenAI, Gemini, Groq, Hugging Face Transformers, Tavily
Custom Model	LoRA Fine-Tuned Gemma Model
Vector Database	FAISS
Real-time Infra	WebSocket

🗃️ Database & Storage

Component	Technologies
Primary Database	Supabase PostgreSQL
ORM & Realtime	Prisma ORM, Supabase (Auth & Realtime)

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✨ Key Features

🤖 AI-Powered Learning

Feature	Description
Intelligent Content Gen.	Dynamic lecture planning, content summarization, class-chat AI
Personalized Learning	Progress analytics, performance insights
AI Counselor	Personalized feedback based on progress and performance

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✒️ AI-Assisted Exam System

Feature	Description
Question Generation	Teachers upload class content (PDF, notes); AI generates MCQs with options, answers, hints, and solutions.
Exam Creation	Auto-generates structured exams from approved questions.
Anti-Cheat System	Clipboard tracking, tab-switch detection, and webcam-based eye movement monitoring.
Student Analysis	Per-question time tracking and topic-wise strength/weakness analysis post-exam.
Class Analytics	Teachers get comprehensive performance metrics across the class.

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🧠 Viva & Evaluation Tools

Feature	Description
AI Viva Simulation	Real-time oral exam simulation, AI verbal response & feedback
Instant Evaluation	Real-time assessment with improvement suggestions

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🧾 Interactive & Collaborative Tools

Feature	Description
Virtual Whiteboard	AI-powered, interactive & collaborative
Shared Docs & Brainstorming	Multi-user real-time collaboration tools
Diagram & Summary Tools	Instant class diagram & summary generation

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🔍 AI Paradigms

Paradigm	Capabilities
Retrieval-Augmented Generation (RAG)	Semantic chunking, metadata enrichment, vector search, context retrieval
Agentic Architecture	Multi-agent task decomposition, parallelism, error handling, web crawling
LangGraph Workflows	Graph-based execution, state management, error recovery

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📚 API Documentation

Content Generation

Endpoint	Description
POST /api/content-gen/pdf/generate	Generate lecture content in PDF format

Lecture Planning

Endpoint	Description
POST /api/lecture-planner/generate	Generate new lecture plan
GET/DELETE /api/lecture-planner/{plan_id}	Retrieve/Delete specific plan
PUT /api/lecture-planner/{plan_id}/topics	Update lecture topics
PUT /api/lecture-planner/{plan_id}/teaching-methods	Update teaching methods
PUT /api/lecture-planner/{plan_id}/resources	Update resources
PUT /api/lecture-planner/{plan_id}/learning-objectives	Update learning objectives

Question Generation

Endpoint	Description
POST /api/q-gen/upload	Upload PDF to generate questions
GET /api/q-gen/status/{job_id}	Check generation status
GET /api/q-gen/questions/{job_id}	Retrieve generated questions

Viva Examination

Endpoint	Description
POST /api/viva/start	Start a new viva session
POST /api/viva/chat	Process interaction during viva
POST /api/viva/cleanup	Clean up session data and audio

Web Search & Memory

Endpoint	Description
POST /api/web-search/search	Perform AI-enhanced web search
POST /api/web-search/memory-stats	Retrieve memory and user profile info
POST /api/web-search/feedback	Submit feedback for AI search responses

[Incomplete Documentation]

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🔧 Sample Requests

📄 Generate PDF Content

{
  "topic": "Introduction to Quantum Computing",
  "additional_context": "Focus on basic concepts",
  "sections": ["Overview", "Key Concepts", "Applications"],
  "llm_provider": "openai"
}

🎤 Start VIVA Session

{
  "subject": "Computer Science",
  "topic": "Data Structures",
  "difficulty": "medium",
  "voice": "onyx"
}

🔎 Web Search

{
  "message": "Explain quantum computing basics",
  "conversation_id": "conv_123",
  "context": {
    "user_level": "beginner"
  }
}

[Incomplete Documentation]

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🚀 Getting Started

1. Prerequisites

Node.js >= 20
Python >= 3.11

2. Frontend Setup

cd frontend
pnpm install
pnpm run db
pnpm run dev:all

3. Backend Setup

cd agents
python -m venv .venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt
python app.py

4. Environment Setup

cp .env.example .env
# Fill in your environment variables

5. Access the Platform

• Frontend: http://localhost:3000

• Backend: http://localhost:5000

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👨‍💻 Contributors

✒️	Name	GitHub
	Faysal Mahmud	Faysal-star
	Iqbal Mahamud	iq-bal
	Raufun Ahsan	taut0logy
	Abir Rahman	abirzishan32
	MD Sakibur Rahman	SakiburRahman07

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✨ Made with ❤️ by the SynapseEd Team ✨