Micro Learning Engine

A personalized micro-learning platform that adapts to your competence and engagement levels, powered by AI and built for a Hackathon.

Features

AI-Powered Personalization: Content adapts based on learning style and competence level
Micro-Learning Focus: Short, focused sessions that maximize retention
Real-time Adaptation: Content adjusts based on engagement patterns
Progress Tracking: Detailed analytics on learning journey and competence growth
Competence Assessment: Continuous evaluation of learning progress
Engagement Analysis: Real-time tracking of user engagement and attention

Tech Stack

Frontend: Next.js 14, React, TypeScript, Tailwind CSS
Backend: Next.js API Routes, Supabase
AI/ML: Groq API (Llama), Google Gemini API
Database: Supabase (PostgreSQL)
Authentication: Supabase Auth
UI Components: Framer Motion, Lucide React

Prerequisites

Before you begin, ensure you have the following installed:

Node.js 18+
npm or yarn
A Supabase account
Groq API key
Google Gemini API key

Quick Start

1. Clone the Repository

git clone <repository-url>
cd micro-learning-engine

2. Install Dependencies

npm install
# or
yarn install

3. Environment Setup

Copy the environment template and fill in your API keys:

cp env.template .env.local

Update .env.local with your actual API keys:

# Supabase Configuration
NEXT_PUBLIC_SUPABASE_URL=your_supabase_project_url_here
NEXT_PUBLIC_SUPABASE_ANON_KEY=your_supabase_anon_key_here
SUPABASE_SERVICE_ROLE_KEY=your_supabase_service_role_key_here

# Groq API Configuration
GROQ_API_KEY=your_groq_api_key_here
GROQ_MODEL_ID=llama3-8b-8192

# Gemini API Configuration
GEMINI_API_KEY=your_gemini_api_key_here
GEMINI_MODEL_ID=gemini-pro

# Next.js Configuration
NEXTAUTH_URL=http://localhost:3000
NEXTAUTH_SECRET=your_nextauth_secret_here

# Application Configuration
NEXT_PUBLIC_APP_URL=http://localhost:3000
NODE_ENV=development

4. Database Setup (No SQL Client Required!)

This is the magic part - we'll set up your database using JavaScript! 🎉

npm run setup-db

This command will:

Connect to your Supabase project using the service role key
Create all necessary tables, indexes, and policies
Insert sample learning modules
Set up Row Level Security (RLS)

No SQL client installation needed! Everything is handled through JavaScript and your Supabase environment variables.

5. Start the Development Server

npm run dev

Open http://localhost:3000 in your browser.

Project Structure

micro-learning-engine/
├── app/                    # Next.js app directory
│   ├── api/               # API routes
│   ├── dashboard/         # Dashboard pages
│   ├── learn/             # Learning module pages
│   ├── demo/              # Demo page
│   ├── globals.css        # Global styles
│   ├── layout.tsx         # Root layout
│   └── page.tsx           # Home page
├── components/            # React components
│   ├── LearningCard.tsx   # Module display card
│   ├── QuestionCard.tsx   # Quiz question component
│   └── ProgressBar.tsx    # Progress tracking
├── hooks/                 # Custom React hooks
│   ├── useLearningEngine.ts
│   └── useEngagementTracking.ts
├── lib/                   # Core utilities
│   ├── supabase.ts        # Database client & types
│   ├── groq.ts           # Groq API integration
│   ├── gemini.ts         # Gemini API integration
│   └── learning-engine.ts # Core learning logic
├── scripts/               # Database setup scripts
│   └── setup-database.js  # JavaScript database setup
├── types/                 # TypeScript type definitions
└── public/               # Static assets

Core Features

Learning Engine

The LearningEngine class handles:

User competence tracking
Content personalization
Session management
Progress analytics
Engagement analysis

AI Integration

Groq API: Content generation and competence assessment
Gemini API: Engagement analysis and learning path recommendations

Engagement Tracking

Real-time tracking of:

Scroll depth
Time on page
Click interactions
Pause/resume patterns
Focus time

🔧 API Endpoints

POST /api/learning/generate-content - Generate personalized content
POST /api/learning/assess-competence - Assess user competence
POST /api/learning/analyze-engagement - Analyze engagement patterns
POST /api/learning/recommend-path - Get learning recommendations

Usage

Basic Learning Flow

User starts a learning session
System generates personalized content based on competence level
User interacts with content and answers questions
System tracks engagement and updates competence
System recommends next learning steps

Customization

The learning engine can be customized through:

Competence thresholds
Engagement sensitivity
Content difficulty levels
Learning style preferences

📊 Analytics

Track learning progress with:

Completion rates
Competence scores
Engagement metrics
Learning velocity
Time-to-mastery

Deployment

Vercel (Recommended)

Push your code to GitHub
Connect your repository to Vercel
Add environment variables in Vercel dashboard
Deploy!

Other Platforms

The app can be deployed to any platform that supports Next.js:

Netlify
Railway
DigitalOcean App Platform
AWS Amplify

Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🎉 Hackathon Submission

This project was built with the following deliverables:

Target User + Pain Points

Target: B2C learners, students, professionals seeking skill development
Pain Points: One-size-fits-all content, learning fatigue, lack of personalization

Business Model + Monetization

Freemium Model: Basic features free, premium analytics and advanced AI
Subscription Tiers: Individual, Team, Enterprise
Revenue Streams: Subscriptions, corporate training, API licensing

Plan for Scale

Phase 1: MVP with core features
Phase 2: Mobile app, advanced analytics
Phase 3: Enterprise features, white-label solutions
Phase 4: Global expansion, AI model improvements

Soumilgit / sentient_ai_upgrad_hack