A full‑stack Learning Management System (LMS) built to support teachers and students with structured courses, live lectures, quizzes, community interaction, and intelligent support tools. Designed for scalability, real‑world classroom needs, and future extensibility.
🚀 Deployed Website:
👉 https://lms-by-tle-terminator.vercel.app
LMS by TLE Terminator is a modern web platform that enables:
- Structured course creation & enrollment
- Recorded and live lectures
- Quizzes and assessments
- Community chat & discussion
- AI‑powered doubt resolution
- Attention tracking engine
The platform aims to bridge the gap between teaching plans and classroom execution, especially for resource‑constrained or large‑scale education systems.
- Create and manage courses and curriculum
- Upload video lectures, audio, and PDF notes
- Support for free and paid lectures
- Real‑time live class support
- Low‑latency interaction design
- Extensible to integrate video conferencing tools
- Computer vision–based microservice for analyzing student attentiveness during live lectures
- Processes image frames via API in real time
- Computes:
- Face presence confidence
- Head pose confidence
- Gaze direction confidence
- Aggregates signals into an overall attention score per student
- Integrates seamlessly with live lecture sessions
- Helps instructors monitor engagement and identify attention drops
- Designed as an independent, scalable microservice that can be enabled per course
- Create quizzes per course or lecture
- Track student attempts and scores
- Scalable for objective evaluation
- Course‑specific discussion channels
- Real‑time messaging via WebSockets
- Upvote‑based message relevance and moderation
- Course‑aware AI assistant that answers doubts using lecture context
- Embedding‑based retrieval from course materials
- Independent chat history per student
- Pluggable to local LLMs or API models
The AI Summary Generator automatically creates concise, structured summaries per lecture by processing the audio of the lecture.
- 🎧 Converts lecture audio → text
- 🧠 Generates an AI-based summary for that specific lecture
- 📌 Stores summaries lecture-wise for quick revision
This enables students to revise faster and allows teachers to provide instant, high-quality recap material with zero manual effort.
- Dedicated STEM learning section with interactive quizes and experiments
- Ask any doubt feature powered by AI
- Separate frontend and backend services for modularity
This module is designed to enhance STEM education by providing interactive content and AI assistance specifically tailored for science, technology, engineering, and mathematics subjects.
Frontend
- React (Vite)
- Tailwind CSS
- Redux Toolkit
- Axios
Backend
- Node.js + Express
- MongoDB (Mongoose)
- Socket.IO for realtime chat
- RESTful APIs
AI Layer
- PDF text extraction
- Embedding storage (MongoDB)
- Contextual response generation
- Supports pluggable LLM backends
Attention Engine
- Python + Flask
- OpenCV + MediaPipe
- Deployed as a separate microservice
Stem Learning
- React + Vite frontend for STEM frontend
- Node.js + Express backend for STEM learning
- Dedicated MongoDB collections for STEM courses and progress tracking
- Deployed as separate frontend and backend services
| Layer | Technology |
|---|---|
| Frontend | React, Vite, Tailwind, Redux |
| Backend | Node.js, Express, Socket.IO |
| Database | MongoDB (Mongoose) |
| Realtime | Socket.IO |
| AI | Embeddings + LLM (pluggable) |
| CV Engine | Flask, OpenCV, MediaPipe |
Tested locally for development. Adjust ports and environment variables for production deployments.
git clone https://github.com/Prasoon52/lms-by-tle-terminator.git
cd lms-by-tle-terminatorcd backend
cp .env.example .env # Create your own .env and add required secrets
npm install
npm run devbackend will typically runs on port
8000depending on configuration.
cd ../frontend
cp .env.example .env # Create your own .env and add required secrets
npm install
npm run devLMS frontend dev server typically runs on
localhost:5173and STEM frontend onlocalhost:3000.
cd Attention_Engine
python3 -m venv venv # On Windows use `python -m venv venv`
source venv/bin/activate # On Windows use `venv\Scripts\activate`
cd ../attention_engine
pip install -r requirements.txt
python app.pyAttention Engine runs as a separate microservice (Flask). Configure frontend/backend to call the attention API endpoint when enabled.
- Backend Setup
cd ../stem-backend
cp .env.example .env # Create your own .env and add required secrets
npm install
npm run devSTEM backend will typically runs on port
5001depending on configuration.
- Frontend Setup
cd ../stem-frontend
cp .env.example .env # Create your own .env and add required secrets
npm install
npm run devSTEM frontend dev server typically runs on
localhost:3000.
Make sure you create .env files for backend and frontend as required.
- .env.example files are provided as templates.
- Fill in your own API keys, database URIs, and secrets.
Do not commit .env files or secrets to version control.
- Microservice‑friendly architecture — AI layer and Attention Engine scale independently
- Designed to support thousands of users and multiple institutions
- Suitable for state/district‑level deployments with additional engineering for multi‑tenant isolation, monitoring, and autoscaling
This project demonstrates:
- Real‑world problem solving
- Full‑stack engineering & integrations
- Practical AI usage (embeddings + contextual retrieval)
- Scalable system design with a clear path to production
Built by TLE Terminator — focused on impact, usability, and system‑level thinking.
- Teacher analytics dashboard (engagement, completion, performance)
- Offline‑first mobile app
- Parent & mentor views
- Automated attendance & engagement insights
- Integration with government education systems
- Duals for Quiz assessments
- Gamification of Quiz elements
Contributions are welcome. Suggested workflow:
- Fork the repo
- Create a feature branch (
git checkout -b feature/your-feature) - Commit your changes (
git commit -m "feat: add ...") - Push and open a pull request
Please follow the existing code style (ESLint/Prettier if configured) and include meaningful commit messages.
 Prasson Patel |
 Abhinav Neema |
 Pranav |
 Abhas Nath |