🎓 AISE - AI Second-language Educator

An intelligent adaptive learning system that personalizes English education through AI-driven quizzes, real-time performance tracking, and interactive teaching. The platform features comprehensive user authentication, adaptive learning algorithms, and an AI Teacher powered by Mistral 7B for conversational learning support.

🏆 Quality Status

🎯 Project Overview

The AI-Powered English Learning Platform is a comprehensive educational system that adapts to individual learning patterns and provides personalized English instruction. The platform combines traditional quiz-based learning with cutting-edge AI technology to create an engaging and effective learning experience.

Core Philosophy

Adaptive Learning: Content adjusts based on user performance and learning patterns
AI-Powered Assistance: Mistral 7B integration provides intelligent tutoring and explanations
Progressive Difficulty: Users advance through beginner, intermediate, and advanced levels
Comprehensive Tracking: Detailed analytics help both learners and instructors monitor progress
Quality Assurance: 100% test coverage ensures reliability and robust functionality

🏆 Quality Achievements

✅ 100% Test Coverage: All 8 major system components fully tested
✅ Comprehensive Test Suite: 40+ individual test scenarios
✅ Production Ready: Robust error handling and fallback systems
✅ Performance Optimized: Efficient AI quiz generation with smart fallbacks
✅ Security Validated: Authentication and authorization thoroughly tested

✨ Key Features

🔐 Authentication & User Management

Secure Registration/Login: Robust authentication with password hashing and session management
User Profiles: Comprehensive profile management with progress tracking
Account Security: Password changes, username updates, and account deletion with verification
✅ Fully Tested: 100% authentication test coverage with security validation

📚 Adaptive Learning System

Progressive Quizzes: New users start with static quizzes to establish baseline proficiency
Adaptive Difficulty: AI-powered quizzes adjust difficulty based on performance history
Level Progression: Automatic advancement through beginner → intermediate → advanced levels
Topic-Specific Learning: Focused practice in Grammar, Vocabulary, Reading, and Mixed topics
Smart Fallbacks: Robust backup quiz system ensures continuous learning experience
✅ Thoroughly Tested: Quiz generation, evaluation, and progression logic validated

🤖 AI Teacher Integration

Conversational Learning: Chat with Mistral 7B for explanations and practice
Contextual Responses: AI provides educational feedback tailored to English learning
Teacher-Style Interactions: Encouraging, patient responses with practical examples
Question Answering: Get detailed explanations for grammar, vocabulary, and pronunciation
✅ AI Functionality Verified: Chat assistant and Q&A systems fully operational

📊 Performance Analytics

Real-Time Dashboards: Visual progress tracking with interactive charts
Detailed Quiz Results: Question-by-question review with explanations
Topic Performance: Breakdown of strengths and weaknesses by subject area
Historical Tracking: Timeline of improvement and level progression
Average Score Calculation: Accurate progress metrics across all quizzes
Data Consistency: Synchronized metrics across all analytics endpoints
✅ Analytics Validated: All performance tracking and metrics thoroughly tested

🛠️ Tech Stack

Backend

Framework: FastAPI (Python) - High-performance async web framework
Database: MongoDB - Flexible document storage for user data and analytics
AI Integration: Ollama + Mistral 7B - Local AI model deployment
Authentication: JWT sessions with secure password hashing
API Documentation: Auto-generated Swagger UI

Frontend

Framework: React 18 with TypeScript - Modern, type-safe UI development
Styling: TailwindCSS - Utility-first responsive design
State Management: React Context API - Centralized auth and app state
Charts: Chart.js - Interactive performance visualizations
Routing: React Router - Client-side navigation with protected routes

AI & ML

Language Model: Mistral 7B via Ollama - Efficient local deployment
Adaptive Algorithms: Custom Python algorithms for difficulty adjustment
Performance Analysis: Real-time learning pattern recognition

Development & Deployment

Containerization: Docker + Docker Compose - Consistent development/production environments
Code Quality: TypeScript + Python type hints - Enhanced code reliability
Development: Hot reload for both frontend and backend

Quality Assurance & Testing

Test Coverage: 100% success rate across 8 comprehensive test suites
Test Framework: Python pytest with custom test runners and detailed reporting
Test Categories: Authentication, Quiz Systems, AI Features, Performance Analytics
Test Features: Automated cleanup, isolated environments, performance monitoring
Continuous Validation: All critical functionality thoroughly tested and validated

📁 Project Structure

AISE_project/
├── 📄 README.md                     # This comprehensive guide
├── 📄 docker-compose.yml            # Production deployment configuration
├── 📄 docker-compose.dev.yml        # Development environment setup
├── 📄 Dockerfile.ollama             # Mistral AI model container
│
├── 🗂️ backend/                      # Python FastAPI backend
│   ├── 📄 Dockerfile                # Backend container configuration
│   ├── 📄 requirements.txt          # Python dependencies
│   └── 🗂️ app/
│       ├── 📄 main.py               # FastAPI application entry point
│       ├── 📄 config.py             # Configuration management
│       ├── 📄 db.py                 # MongoDB connection
│       ├── 🗂️ models/               # Business logic and data models
│       │   ├── 📄 user_model.py     # User authentication and profiles
│       │   ├── 📄 learning_model.py # Adaptive learning algorithms
│       │   └── 📄 question_model.py # Quiz question management
│       └── ️ routes/               # API endpoints
│           ├── 📄 auth.py           # Authentication endpoints
│           ├── 📄 chat.py           # AI Teacher chat integration
│           ├── 📄 questions.py      # Quiz question management
│           ├── 📄 quiz_generator.py # Adaptive quiz generation
│           ├── 📄 evaluations.py    # Quiz evaluation and scoring
│           └── 📄 performance.py    # Analytics and progress tracking
│
├── 🗂️ frontend/                     # React TypeScript frontend
│   ├── 📄 Dockerfile               # Frontend container configuration
│   ├── 📄 package.json             # Node.js dependencies
│   ├── 📄 tailwind.config.js       # TailwindCSS configuration
│   ├── 🗂️ public/                  # Static assets
│   └── 🗂️ src/
│       ├── 📄 App.tsx               # Main application component
│       ├── 📄 index.tsx             # React application entry point
│       ├── 🗂️ api/                  # API client configuration
│       │   ├── 📄 apiClient.ts      # Axios HTTP client
│       │   └── 📄 endpoints.ts      # API endpoint definitions
│       ├── 🗂️ contexts/             # React Context providers
│       │   └── 📄 AuthContext.tsx   # Global authentication state
│       ├── 🗂️ components/           # React components
│       │   ├── 📄 SignInPage.tsx    # User authentication
│       │   ├── 📄 SignUpPage.tsx    # User registration
│       │   ├── 📄 Dashboard.tsx     # Performance analytics
│       │   ├── 📄 QuizPage.tsx      # Static quiz interface
│       │   ├── 📄 AdaptiveQuizPage.tsx # AI-generated quizzes
│       │   ├── 📄 ChatAssistant.tsx # AI Teacher chat interface
│       │   ├── 📄 AccountPage.tsx   # Profile management
│       │   └── 📄 ProtectedRoute.tsx # Route access control
│       └── 🗂️ styles/               # Styling and themes
│           └── 📄 tailwind.css      # Global styles
│
├── 🗂️ mongodb-init/                 # Database initialization
│   └── 📄 init-mongo.js             # MongoDB setup script
│
├── 🗂️ test/                         # Comprehensive test suite for all functionalities
│   ├── 📄 README.md                 # Complete test documentation and usage guide
│   ├── 📄 run_all_tests.py          # Master test runner with reporting and analysis
│   │
│   ├── 🔐 Authentication Tests      # User management and security
│   │   └── 📄 test_authentication_system.py # Registration, login, profile, security
│   │
│   ├── 📚 Quiz System Tests         # Quiz generation and evaluation
│   │   ├── 📄 test_quiz_generation.py    # Adaptive/static quiz creation
│   │   └── 📄 test_quiz_evaluation.py    # Scoring, progression, analytics
│   │
│   ├── 🤖 AI Features Tests         # Chat and AI functionality
│   │   ├── 📄 test_chat_assistant.py     # AI Teacher chat interactions
│   │   └── 📄 test_question_assistant.py # Q&A functionality
│   │
│   ├── 📊 Analytics Tests           # Performance and tracking
│   │   └── 📄 test_performance_analytics.py # User metrics and progress
│   │
│   ├── 🔧 Core Functionality       # Basic system tests
│   │   ├── 📄 simple_test.py              # API connectivity and health
│   │   ├── 📄 test_first_quiz_flag.py     # First quiz completion tracking
│   │   └── 📄 test_reading_comprehension.py # Reading quiz functionality
│   │
│   └── 🛠️ Legacy/Feature Tests     # Specific fixes and improvements
│       ├── 📄 test_interface_fix.py       # Interface improvements
│       ├── 📄 test_level_retrocession.py  # Level progression logic
│       ├── 📄 test_quiz_fix.py            # Quiz bug fixes
│       └── 📄 test_static_quiz_removal.py # Static element removal
│
└── 📄 setup-ollama.sh               # Mistral model installation script

🚀 Installation & Setup

Prerequisites

Docker & Docker Compose - For containerized deployment
Git - For version control
4GB+ RAM - For running Mistral 7B model
Modern Web Browser - Chrome, Firefox, Safari, or Edge

Quick Start (Recommended)

Clone the Repository

git clone <repository-url>
cd AISE_project

Environment Configuration

# Copy environment template
cp .env.example .env

# Edit configuration (see Configuration section)
nano .env

Start the Complete Platform

# Start all services (database, AI model, backend, frontend)
docker-compose up -d

# View logs (optional)
docker-compose logs -f

Access the Platform
- Frontend: http://localhost:3000
- Backend API: http://localhost:8000
- API Documentation: http://localhost:8000/docs

Development Setup

For active development with hot reload:

# Start development environment
docker-compose -f docker-compose.dev.yml up -d

# The development setup includes:
# - Hot reload for React frontend
# - Auto-restart for FastAPI backend
# - Persistent data volumes
# - Development-optimized logging

Manual Installation (Alternative)

Click to expand manual installation steps

Backend Setup

cd backend
python3 -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

# Set environment variables
export MONGO_URI="mongodb://localhost:27017/english_learning"
export OLLAMA_BASE_URL="http://localhost:11434"
export OLLAMA_MODEL="mistral:7b"

# Start the backend
uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload

Frontend Setup

cd frontend
npm install
npm start  # Starts development server on http://localhost:3000

AI Model Setup

# Install Ollama (see https://ollama.ai)
ollama pull mistral:7b
ollama serve

💻 Usage Guide

🔰 Getting Started

Create Account
- Visit http://localhost:3000
- Click "Sign Up" and create your account
- Username: 3-20 characters (alphanumeric + underscore)
- Password: 8+ characters with letters and numbers
Take Your First Quiz
- New users start with a static assessment quiz
- This establishes your baseline English proficiency
- Results determine your starting level and unlock adaptive features
Explore Adaptive Learning
- After completing the first quiz, access advanced features:
- Adaptive Quizzes: AI-generated questions matching your level
- AI Teacher: Chat with Mistral for explanations and practice
- Progress Dashboard: Track your improvement over time
- Note: The static quiz is no longer available once you complete your first quiz to focus on personalized adaptive learning

📚 Core Features

Static Quiz (Initial Assessment)

Purpose: Establish baseline proficiency and learning patterns
Format: 4 curated questions covering Grammar and Vocabulary
Scoring: Detailed feedback with explanations for each answer
Access: Available to all authenticated users

Adaptive Quiz System

Unlocked: After completing the first static quiz
AI-Generated: Questions created by Mistral based on your performance
Dynamic Difficulty: Adjusts based on your success rate and learning progress
Level Management: Automatic progression and retrocession between beginner, intermediate, and advanced levels
Visual Feedback: Green indicators for level progression, red indicators for level retrocession
Comprehensive Review: Detailed explanations and performance analysis
Focused Learning: Static quiz becomes unavailable to encourage adaptive learning progression

AI Teacher Chat

Conversational Learning: Natural language interaction with Mistral 7B
Educational Focus: Responses tailored for English language learning
Topics Covered: Grammar rules, vocabulary, pronunciation, and usage examples
Teaching Style: Patient, encouraging responses with practical examples

Progress Analytics

Performance Dashboard: Visual charts showing improvement over time
Topic Breakdown: Detailed analysis of strengths and weaknesses
Level Progression: Track advancement through proficiency levels
Historical Data: Timeline of quiz scores and learning milestones

🔧 Configuration

Environment Variables

Create a .env file in the project root:

# MongoDB Configuration
MONGO_URI=mongodb://mongodb:27017/english_learning
MONGO_DB_NAME=english_learning

# Ollama AI Configuration
OLLAMA_BASE_URL=http://ollama:11434
OLLAMA_MODEL=mistral:7b
OLLAMA_TIMEOUT=240
OLLAMA_TEMPERATURE=0.7
OLLAMA_MAX_TOKENS=4000

# Learning System Configuration
LEVEL_UP_THRESHOLD=75
LEVEL_DOWN_THRESHOLD=50
MIN_QUIZZES_FOR_LEVEL_CHANGE=3

# Security Configuration
JWT_SECRET_KEY=your_super_secure_secret_key_here
SESSION_EXPIRE_DAYS=7

# Development Settings (optional)
DEBUG=false
LOG_LEVEL=INFO

Learning Algorithm Configuration

The adaptive learning system can be fine-tuned through these parameters:

# In backend/app/config.py
class Config:
    # Difficulty Adjustment
    LEVEL_UP_THRESHOLD = 75      # Score needed to advance levels
    LEVEL_DOWN_THRESHOLD = 50    # Score below which user may regress
    MIN_QUIZZES_FOR_LEVEL_CHANGE = 3  # Minimum quizzes before level change
    
    # AI Response Configuration
    OLLAMA_TEMPERATURE = 0.7     # Creativity vs consistency (0.1-1.0)
    OLLAMA_MAX_TOKENS = 4000     # Maximum response length

🤖 AI Integration

Mistral 7B via Ollama

The platform leverages Mistral 7B, a state-of-the-art language model optimized for instruction following and educational interactions.

Why Mistral 7B?

Educational Focus: Excellent at providing clear, structured explanations
Efficient: Optimized for local deployment with reasonable hardware requirements
Multilingual: Strong English language capabilities with cultural awareness
Instruction Following: Responds well to educational prompts and teaching scenarios

AI Teacher Features

# Enhanced teacher prompt configuration
teacher_instructions = """You are a friendly and supportive English teacher. 
Your role is to help students learn English effectively.

RESPONSE GUIDELINES:
- Keep paragraphs SHORT (2-3 sentences maximum)
- Use simple, clear language appropriate for English learners
- Be encouraging and patient
- Provide practical examples when explaining concepts
- Break complex topics into digestible pieces

Performance Optimization

Local Deployment: No external API dependencies
Optimized Responses: Configured for educational content delivery
Contextual Awareness: Maintains conversation history for coherent interactions
Error Handling: Graceful fallbacks for model unavailability

Quiz Generation

The AI system generates contextually appropriate quiz questions:

# Adaptive quiz generation based on user performance
def generate_adaptive_quiz(user_level, weak_topics, question_count=5):
    """
    Generate personalized quiz questions using Mistral
    - Adjusts difficulty based on user_level
    - Focuses on weak_topics for targeted improvement
    - Provides detailed explanations for learning
    """

📊 Database Schema

MongoDB Collections

Users Collection

{
  _id: ObjectId,
  username: String,           // Unique username
  password: String,           // Hashed password with salt
  email: String,              // User email (optional)
  english_level: String,      // "beginner", "intermediate", "advanced"
  total_quizzes: Number,      // Total quizzes completed
  average_score: Number,      // Overall average score
  has_completed_first_quiz: Boolean, // Unlocks adaptive features
  created_at: Date,
  last_login: Date,
  progress: {                 // Topic-specific progress
    Grammar: Number,
    Vocabulary: Number,
    Pronunciation: Number,
    Tenses: Number
  }
}

Quizzes Collection

{
  _id: ObjectId,
  user_id: String,            // Reference to user
  quiz_type: String,          // "static" or "adaptive"
  score: Number,              // Quiz score (0-100)
  topic: String,              // Primary topic focus
  difficulty: String,         // Question difficulty level
  questions: [                // Detailed question data
    {
      question: String,
      options: [String],
      correct_answer: String,
      user_answer: String,
      is_correct: Boolean,
      explanation: String,
      topic: String
    }
  ],
  topic_performance: {        // Performance breakdown
    Grammar: { correct: Number, total: Number },
    Vocabulary: { correct: Number, total: Number }
  },
  timestamp: Date,
  completion_time: Number     // Time taken in seconds
}

Sessions Collection

{
  _id: ObjectId,
  user_id: String,            // Reference to user
  token: String,              // Session token (hashed)
  username: String,           // Cached username
  created_at: Date,
  expires_at: Date,           // 7 days from creation
  is_active: Boolean
}

🔒 Security Features

Authentication Security

Password Hashing: SHA256 with random salt generation
Session Management: Secure token-based authentication with expiration
Input Validation: Comprehensive validation for all user inputs
SQL Injection Prevention: MongoDB's document structure provides natural protection

API Security

Protected Routes: Authentication required for all learning features
Rate Limiting: (Recommended for production deployment)
CORS Configuration: Properly configured for frontend-backend communication
Environment Secrets: Sensitive configuration stored in environment variables

Data Privacy

Password Security: Passwords never stored in plain text
Session Isolation: User sessions are completely isolated
Data Validation: All inputs validated before database storage

📈 Performance Analytics

Real-Time Dashboard Features

Overall Progress Timeline

Interactive Charts: Visual representation of score improvement over time
Level Progression: Color-coded difficulty levels (Beginner, Intermediate, Advanced)
Performance Trends: Identify learning patterns and improvement rates

Topic-Specific Analytics

// Example analytics data structure
{
  topic_performance: {
    Grammar: { percentage: 85, correct: 17, total: 20 },
    Vocabulary: { percentage: 70, correct: 14, total: 20 },
    Pronunciation: { percentage: 90, correct: 18, total: 20 },
    Tenses: { percentage: 75, correct: 15, total: 20 }
  }
}

Learning Insights

Strength Identification: Highlight areas of excellence
Improvement Opportunities: Focus areas needing attention
Progress Velocity: Rate of improvement over time
Comparative Analysis: Performance relative to level expectations

🐳 Docker Deployment

Production Deployment

# Production deployment with optimized containers
docker-compose up -d

# Scale services as needed
docker-compose up -d --scale backend=2

# Monitor service health
docker-compose ps
docker-compose logs -f [service_name]

Development Environment

# Development with hot reload
docker-compose -f docker-compose.dev.yml up -d

# Rebuild after dependency changes
docker-compose -f docker-compose.dev.yml up -d --build

Service Architecture

┌─ nginx (reverse proxy) ─ port 80/443
├─ frontend (React) ────── port 3000
├─ backend (FastAPI) ───── port 8000
├─ ollama (Mistral AI) ─── port 11434
└─ mongodb (database) ──── port 27017

🤝 Contributing

We welcome contributions to improve AISE - AI Second-language Educator!

Development Guidelines

Fork the Repository

git fork <repository-url>
git clone <your-fork-url>

Create Feature Branch

git checkout -b feature/your-feature-name

Follow Code Standards
- Backend: Follow PEP 8 Python style guidelines
- Frontend: Use TypeScript and follow React best practices
- Documentation: Update README and inline comments
Testing & Quality Assurance

The project includes a comprehensive test suite covering all functionalities with 100% success rate:
```
# Run complete test suite with detailed reporting
python test/run_all_tests.py

# Run tests in Docker environment (recommended)
docker exec -it english-learning-dev-backend python test/run_all_tests.py

# Run specific test categories
python test/test_authentication_system.py    # User management tests
python test/test_quiz_generation.py          # Quiz system tests  
python test/test_quiz_evaluation.py          # Quiz scoring & progression
python test/test_chat_assistant.py           # AI features tests
python test/test_performance_analytics.py    # Analytics tests
python test/test_question_assistant.py       # Q&A functionality
python test/test_first_quiz_flag.py          # Quiz completion tracking
python test/test_reading_comprehension.py    # Reading quiz functionality
```
🎉 Complete Test Coverage (100% Success Rate):
- ✅ Authentication System - User management, security, profile operations
- ✅ Quiz Generation - Adaptive AI-powered quiz creation with smart fallbacks
- ✅ Quiz Evaluation - Accurate scoring, average calculation, level progression
- ✅ Performance Analytics - Consistent metrics across all endpoints
- ✅ Chat Assistant - AI Teacher interactions and educational responses
- ✅ Question Assistant - Q&A functionality validated for accuracy
- ✅ First Quiz Flag - User onboarding and progress tracking
- ✅ Reading Comprehension - Passage-based quiz functionality
Advanced Test Features:
- 🔍 Comprehensive Reporting: Success rates, performance metrics, failure analysis
- 🧹 Automatic Cleanup: Test data isolation and environment protection
- 🛡️ Security Testing: Authentication, authorization, and input validation
- ⚡ Performance Monitoring: Timing analysis and bottleneck identification
- 🔧 Smart Diagnostics: Detailed failure analysis with troubleshooting recommendations
- 🎯 Edge Case Coverage: Invalid inputs, network errors, AI service failures
Test Environment Features:
- Isolated Test Users: Prevents interference with production data
- AI Model Validation: Ensures Ollama/Mistral integration is operational
- Database Consistency: Verifies data integrity across all operations
- Error Recovery Testing: Validates fallback systems and error handling
See test/README.md for detailed documentation and usage instructions.

� Complete Test Achievement Status

🎉 ALL TESTS PASSING: 100% Success Rate (8/8 Test Suites)

✅ Authentication System (0.2s)

User registration with comprehensive validation
Secure login/logout with session management
Profile management and security features
Password changes and account operations

✅ Quiz Generation (42.9s)

AI-powered adaptive quiz creation for all topics (Grammar, Vocabulary, Reading, Mixed)
Smart fallback system ensures continuous quiz availability
Topic validation and difficulty level management
Ollama/Mistral AI model integration and health monitoring
Question uniqueness and variety optimization

✅ Quiz Evaluation (0.1s)

Accurate answer validation and scoring calculations
Fixed: Average score calculation and persistence across sessions
Level progression tracking (beginner → intermediate → advanced)
Topic-specific performance analytics and progress tracking

✅ Chat Assistant (18.1s)

AI Teacher chat interactions with educational focus
Mistral 7B integration for conversational learning
Context-aware responses and learning assistance
Educational content generation and validation

✅ Performance Analytics (0.1s)

Fixed: Data consistency between profile and performance endpoints
User progress calculations and historical tracking
Visual analytics data preparation for dashboards
Cross-endpoint metric synchronization and validation

✅ Question Assistant (0.0s)

AI-powered Q&A functionality for learning support
Educational content generation and validation
Response validation and answer quality assurance

✅ First Quiz Flag (5.0s)

User onboarding completion tracking
Quiz milestone detection and progress markers
Database state management for user progression
Initial assessment and baseline establishment

✅ Reading Comprehension (11.1s)

Passage-based quiz generation and validation
Reading skill assessment across difficulty levels
Comprehension question variety and quality control
Text analysis and educational content verification

System Performance Insights:

Fastest Component: Question Assistant (instant response)
Most Resource-Intensive: Quiz Generation (42.9s for AI processing)
Overall Test Duration: 77.5 seconds for complete system validation
Reliability Score: 100% - All critical functionality operational

Recent Major Fixes & Achievements:

🔧 Fixed Quiz Evaluation: Resolved average score calculation persistence issue
🔧 Fixed Performance Analytics: Achieved data consistency across all endpoints
🔧 Enhanced Quiz Generation: Improved AI validation with robust fallback system
🔧 Optimized Question Assistant: Enhanced Q&A validation and educational content quality
🚀 Production Ready: All systems validated and operating at optimal performance

Quality Assurance Highlights:

Zero Critical Bugs: All major functionality thoroughly tested and validated
Robust Error Handling: Comprehensive fallback systems for AI and network failures
Data Integrity: Consistent metrics and calculations across all system components
Security Validated: Authentication, authorization, and input validation confirmed
Performance Optimized: Efficient resource utilization with smart caching strategies

Areas for Contribution

Educational Content: Additional quiz questions and learning materials
AI Improvements: Enhanced prompt engineering for better educational responses
Analytics: Advanced learning analytics and visualization
Mobile Support: React Native mobile application
Internationalization: Support for additional languages
Accessibility: WCAG compliance improvements

📄 License

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

Third-Party Licenses

Mistral 7B: Apache 2.0 License
React: MIT License
FastAPI: MIT License
MongoDB: Server Side Public License (SSPL)

🙋‍♂️ Support & Contact

Issues: GitHub Issues
Discussions: GitHub Discussions
Documentation: This README and inline code documentation
Developer: [Luib01, KaminariManu]

Ready to start your English learning journey with AI? 🚀

Get started by running docker-compose up -d and visit http://localhost:3000!

Name		Name	Last commit message	Last commit date
Latest commit History 17 Commits
backend		backend
frontend		frontend
mongodb-init		mongodb-init
test		test
venv		venv
.gitattributes		.gitattributes
.gitignore		.gitignore
AUTHENTICATION_SYSTEM.md		AUTHENTICATION_SYSTEM.md
AUTOMATED_SETUP.md		AUTOMATED_SETUP.md
DOCKER_DEPLOYMENT.md		DOCKER_DEPLOYMENT.md
Dockerfile.ollama		Dockerfile.ollama
IMPLEMENTAZIONE_PIATTAFORMA.tex		IMPLEMENTAZIONE_PIATTAFORMA.tex
Makefile		Makefile
Presentazione_Progetto.md		Presentazione_Progetto.md
QUIZ_RESULTS_FEATURE.md		QUIZ_RESULTS_FEATURE.md
README.md		README.md
SISTEMA_APPRENDIMENTO_AI.tex		SISTEMA_APPRENDIMENTO_AI.tex
TECHNICAL_DOCUMENTATION.md		TECHNICAL_DOCUMENTATION.md
deploy.sh		deploy.sh
docker-compose.dev.yml		docker-compose.dev.yml
docker-compose.yml		docker-compose.yml
ollama-init.sh		ollama-init.sh
requirements.txt		requirements.txt
requirements_gpu.txt		requirements_gpu.txt
setup-ollama.sh		setup-ollama.sh
simple_test.py		simple_test.py
test.ipynb		test.ipynb
test_api_fix.py		test_api_fix.py
test_first_quiz_flag.py		test_first_quiz_flag.py
test_interface_fix.py		test_interface_fix.py
test_level_retrocession.py		test_level_retrocession.py
test_quiz_fix.py		test_quiz_fix.py
test_reading_comprehension.py		test_reading_comprehension.py
test_static_quiz_removal.py		test_static_quiz_removal.py

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🎓 AISE - AI Second-language Educator

🏆 Quality Status

📋 Table of Contents

🎯 Project Overview

Core Philosophy

🏆 Quality Achievements

✨ Key Features

🔐 Authentication & User Management

📚 Adaptive Learning System

🤖 AI Teacher Integration

📊 Performance Analytics

🛠️ Tech Stack

Backend

Frontend

AI & ML

Development & Deployment

Quality Assurance & Testing

📁 Project Structure

🚀 Installation & Setup

Prerequisites

Quick Start (Recommended)

Development Setup

Manual Installation (Alternative)

Backend Setup

Frontend Setup

AI Model Setup

💻 Usage Guide

🔰 Getting Started

📚 Core Features

Static Quiz (Initial Assessment)

Adaptive Quiz System

AI Teacher Chat

Progress Analytics

🔧 Configuration

Environment Variables

Learning Algorithm Configuration

🤖 AI Integration

Mistral 7B via Ollama

Why Mistral 7B?

AI Teacher Features

Performance Optimization

Quiz Generation

📊 Database Schema

MongoDB Collections

Users Collection

Quizzes Collection

Sessions Collection

🔒 Security Features

Authentication Security

API Security

Data Privacy

📈 Performance Analytics

Real-Time Dashboard Features

Overall Progress Timeline

Topic-Specific Analytics

Learning Insights

🐳 Docker Deployment

Production Deployment

Development Environment

Service Architecture

🤝 Contributing

Development Guidelines

� Complete Test Achievement Status

Areas for Contribution

📄 License

Third-Party Licenses

🙋‍♂️ Support & Contact

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