🧠 NexusQuiz - AI-Powered Adaptive Quiz Platform

An intelligent Quiz system that dynamically adjusts question difficulty using Machine learning. Features real-time performance adaptation, comprehensive analytics, and a stunning glassmorphism UI. The backend is Dockerized and built with FastAPI, Firebase, and modern JavaScript.

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🌟 Key Features

🤖 AI-Powered Adaptation

• Machine Learning Difficulty Adjustment - Logistic Regression model predicts optimal question difficulty

• Real-time Performance Analysis - Adapts question difficulty based on user responses

• Personalized Learning Paths - Customized quiz experience for each user

🎯 Smart Quiz Engine

• Dynamic Difficulty Levels - Easy, Medium, Hard questions adjusted in real-time

• Progress Tracking - Real-time performance metrics and analytics

• Adaptive Scoring - Intelligent scoring system that rewards improvement

• Auto Grading System - Instant answer evaluation and scoring

💫 Modern UI/UX

• Glassmorphism Design - Stunning frosted glass aesthetic with particle animations

• Responsive Interface - Beautiful design that works on all devices

• Smooth Animations - Engaging user experience with fluid transitions

🔧 Advanced Features

• Admin Dashboard - Comprehensive question bank management

• API Integration - Automatic question import from Open Trivia DB

• Real-time Analytics - Detailed performance insights and statistics

• User Dashboard - Progress visualization and tracking

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🛠️ Tech Stack

Backend & AI

• Python - Core programming language

• FastAPI - Modern, high-performance web framework

• Docker – Containerized backend for consistent and scalable deployment

• Scikit-Learn - Machine learning for difficulty prediction

• Firebase Firestore - NoSQL database for real-time data

Frontend & UI

• Vanilla JavaScript - Interactive frontend logic

• HTML5 & CSS3 - Responsive web design with glassmorphism effects

• Particle Animations - Dynamic background elements

Machine Learning

• Logistic Regression - Difficulty classification model

• Real-time Analytics - Performance pattern recognition

• Predictive Modeling - Skill level assessment

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📂 Project Structure

Nexus-Quiz/
├── quiz_engine/                 # AI & ML Components
│   ├── difficulty_model.py     # ML model for difficulty prediction
│   ├── selector.py             # Question selection logic
│   ├── grader.py               # Answer evaluation system
│   └── feedback_generator.py   # Personalized feedback generation
├── backend/                    # API & Server
│   ├── main.py                # FastAPI application entry point
│   ├── models/                # Pydantic data models
│   ├── routes/                # API route handlers
│   │   ├── quiz.py           # Quiz management endpoints
│   │   ├── user.py           # User authentication & management
│   │   ├── question.py       # Question bank operations
│   │   └── result.py         # Results & analytics
│   └── db/                    # Database configuration
│       └── firebase_config.py # Firebase integration
├── frontend/                   # User Interface
│   ├── index.html            # Main application
│   ├── style.css             # Glassmorphism styles
│   └── app.js               # Frontend logic
├── Dockerfile                 # Backend container definition
├── docker-compose.yml         # Multi-container orchestration
└── requirements.txt          # Python dependencies

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🚀 Adaptive Quiz Flow

Process Overview:

• Quiz Initialization - System assesses user's starting level based on previous performance

• Real-time Assessment - Each answer updates skill evaluation and ML model

• ML-Powered Adaptation - Logistic regression predicts optimal difficulty for next question

• Progressive Challenge - Questions dynamically adapt to user capability

• Comprehensive Evaluation - Final scoring with detailed analytics and AI-generated feedback

• Continuous Learning - System improves from aggregated user data

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🔧 Installation & Setup

🐳 Docker Setup (Recommended)

Prerequisites

• Docker
• Docker Compose

Run Backend Using Docker

docker compose up --build

Backend will be available at:

http://localhost:8000

Swagger API Docs:

http://localhost:8000/docs

Stop Containers

docker compose down

✅ No Python, virtual environment, or manual dependency setup required

✅ Firebase credentials are securely mounted via Docker volumes

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OR

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Prerequisites

• Python 3.8 or higher

• Firebase account and project

• Modern web browser

Quick Start

Clone Repository

git clone https://github.com/MuhammadUsman-Khan/Nexus-Quiz.git
cd Nexus-Quiz

Install Dependencies

pip install -r requirements.txt

Configure Firebase

• Create Firebase project at Firebase Console

• Download serviceAccountKey.json and place in backend/db/

• Update security rules as needed

Run Application

# Start Backend Server
cd backend
uvicorn main:app --reload --port 8000

# Start Frontend (in new terminal)
python -m http.server 3000
Access Platform

Frontend: http://localhost:3000

API Documentation: http://localhost:8000/docs

🎮 Usage Guide

For Students/Learners

• Register Account - Create your personalized profile with secure authentication

• Take Adaptive Quiz - System automatically adjusts to your skill level in real-time

• View Real-time Progress - Watch your performance metrics update after each question

• Receive AI Feedback - Get personalized improvement suggestions based on performance patterns

• Track Learning Journey - Monitor progress over time with detailed analytics

For Administrators/Educators

• Manage Question Bank - Add, edit, or import questions with metadata

• Import from Open Trivia DB - Automatic question collection from external API

• Monitor System Analytics - Overall platform performance and usage statistics

• User Progress Oversight - Track individual and group learning progress

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🔌 API Endpoints

Quiz Management

Method      	Endpoint	                      Description
POST	  /api/quiz/start            	Initialize new adaptive quiz session
POST	  /api/quiz/submit-answer	    Evaluate answer and update user model
POST	  /api/quiz/next-question    	Get next question based on current performance
POST	  /api/quiz/end-quiz	        Finalize session and generate feedback

User Management

Method	    Endpoint	                        Description
POST	  /api/users/register	    User registration with secure authentication
POST	  /api/users/login	        User login and session management
GET	      /api/users/{email}	    Retrieve user profile and history

Question Bank

Method  	    Endpoint  	                                Description
GET	        /api/questions/all	                  Retrieve all questions (Admin only)
POST	    /api/questions/add	                  Add new question to bank
POST	    /api/questions/import-from-api	      Bulk import from Open Trivia DB
DELETE	    /api/questions/{id}	                  Remove question from bank

Analytics & Results

Method  	    Endpoint	                            Description
GET	      /api/results/user/{email}	      Get user's quiz history and progress
GET  	  /api/results/all	              System-wide analytics (Admin only)

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🤖 Machine Learning Implementation

Difficulty Prediction Model

class DifficultyModel:
    def __init__(self):
        self.model = LogisticRegression()
        # Trained on user performance data
    
    def predict_difficulty(self, previous_score: float) -> str:
        prediction = self.model.predict([[previous_score]])
        return ["easy", "medium", "hard"][prediction[0]]

Adaptive Logic Features

• Input Parameters: User performance history, current score, response time, accuracy patterns

• ML Processing: Logistic regression with feature engineering and real-time retraining

• Output: Optimal difficulty level ("easy", "medium", "hard") for next question

• Continuous Learning: Model improves automatically from aggregated user data

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🎨 UI/UX Features

Glassmorphism Design

• Frosted Glass Effects: Backdrop filters for modern aesthetic

• Particle Animations: Dynamic floating elements in background

• Smooth Transitions: CSS animations for engaging interactions

• Responsive Grid: Adaptive layout for all device sizes

User Experience

• Real-time Feedback: Immediate response after each answer

• Progress Visualization: Clear indicators of current performance

• Intuitive Navigation: Easy-to-use interface for all user types

🔒 Security Features

• Password Hashing: PBKDF2 with HMAC-SHA256 and salt

• Input Validation: Pydantic models for data integrity and type safety

• CORS Protection: Configured cross-origin resource sharing

• Firebase Security Rules: Database-level access control

• API Authentication: Header-based user verification

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🚀 Deployment

Local Development

# Backend Development Server
uvicorn main:app --reload --host 0.0.0.0 --port 8000

# Frontend Development Server
python -m http.server 3000

Production Deployment

• Backend: Deploy FastAPI to cloud platforms (Heroku, DigitalOcean, AWS, GCP)

• Frontend: Deploy static files to hosting services (Netlify, Vercel, GitHub Pages)

• Database: Firebase Firestore (automatically scales with usage)

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📈 Future Enhancements

🎮 Gamification Features

• Leaderboards - Competitive ranking system

• Achievement Badges - Reward system for milestones

• Points & Rewards - Engagement激励机制

• Social Features - Peer learning and competition

🔍 Advanced Analytics

• Detailed Performance Breakdowns - Subject-wise analysis

• Comparative Analytics - Peer performance comparison

• Predictive Performance Modeling - Future performance forecasts

• Learning Pattern Recognition - Study habit insights

🤖 AI Expansion

• LLM Integration - AI-generated questions and explanations

• Natural Language Processing - Open-ended answer evaluation

• Advanced Recommendation System - Personalized learning paths

• Speech Recognition - Voice-based answering

📱 Platform Growth

• Mobile Application - iOS and Android native apps

• Multi-language Support - Internationalization

• LMS Integration - Compatibility with learning management systems

• Offline Capability - Quiz functionality without internet

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License Information

This project has no license specified. All rights are reserved by the owner.

🤝 Contributing

We welcome contributions! Please follow these steps:

Fork the repository

Create a feature branch (git checkout -b feature/amazing-feature)

Commit your changes (git commit -m 'Add amazing feature')

Push to the branch (git push origin feature/amazing-feature)

Open a Pull Request

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👨‍💻 Developer

Muhammad Usman Khan

GitHub: @MuhammadUsman-Khan

Project: Adaptive Quiz Platform with Machine Learning

Email: m.usman.khan.stu@gmail.com*