AI E-Learning Platform

Learning, connected in real time.

A MERN learning platform combining facial recognition, emotion detection, classroom recommendations, real-time conversations, and an AI chatbot.

Explore the project Source code

ReactNode.jsExpress.jsMongoDBWebSocketMachine LearningGoogle OAuthCI/CD

The problem

A digital learning environment needs more than course content: students also need interaction, access, and relevant resources. This project brings AI-assisted learning features and live communication into one web platform.

Project context

Education / AI integration / MERN

Implementation scope

Facial recognition and emotion detection using machine learning.
GitHub and Google Classroom recommendations, plus an AI chatbot.
WebSocket chatroom, Google OAuth, and a CI/CD deployment workflow.

Project showcase

e-learning-platform-with-ai.concept.png

AI-generated project illustration, not an application screenshot. Any displayed metrics are illustrative.

System architecture

01Sign in
02Learning space
03AI support
04Live chat
05Delivery

Learning interface

React

The frontend layer for learner interaction, recommendations, and conversations.

Application & data

Node.js / Express.js / MongoDB

The MERN backend and database foundation for the learning platform.

Intelligent features

Machine Learning / AI chatbot

Facial recognition, emotion detection, and GitHub/Google Classroom recommendations.

Connected delivery

WebSocket / Google OAuth / CI/CD

Real-time chat, Google sign-in integration, and automated delivery workflows.

Engineering considerations

Keep AI supportive, not authoritative.

Emotion detection should be treated as a model inference, not a reliable diagnosis or a definitive assessment of a learner. Clear communication of uncertainty is important for this type of feature.

Design for live conversations.

A WebSocket chatroom introduces connection state, message ordering, and reconnect considerations alongside the regular HTTP application flow.

Treat identity and facial data carefully.

Google OAuth and facial recognition introduce different privacy boundaries. Consent, data minimization, and protection of identity information are important review points.

Technical focus

Combining MERN application development with machine-learning features.
Connecting real-time communication with an educational user experience.
Bringing authentication and CI/CD into a full-stack project.

Possible extensions

Possible extensions include accessibility testing, clearer AI consent controls, and evaluating recommendation relevance. These are improvement opportunities, not claims of completed work.