CLI based Adaptive Learning Game (supports five subject)

AI-Powered Dynamic Quiz Generator with Adaptive Difficulty, ML Engine & Performance Tracking

This project is an interactive command-line learning game that generates dynamic, subject-specific MCQs using the OpenAI API. It includes a machine-learning adaptive engine, a performance tracker, and visual performance graphs to create a personalized learning experience.

---

Architecture

Features

1. AI-Generated MCQs (OpenAI API)

• Dynamically generates high-quality MCQs across subjects like Math, Science, History, Geography, and English.

• Ensures:

  • 4 consistent answer choices
  • Fully structured JSON output
  • Explanation for each question
  • Difficulty score (1–10)

---

2. Adaptive Difficulty Engine (ML-Powered)

• Trained on a synthetic dataset that includes:

  • Accuracy %
  • Time taken
  • Streak
  • Subject
  • Current level

• Uses Logistic Regression + StandardScaler to recommend next difficulty level:

  • Easy → Medium
  • Medium → Hard
  • Hard → Medium/Easy (if user struggles)

---

3. Performance Tracking & Graphs

Tracks:

• Accuracy over time
• Time taken per question
• Streaks
• Overall summary

Plots include:

• Accuracy trend line
• Response time graph

Uses matplotlib to render graphs.

---

4. CLI-Based Game Flow

Simple, clean terminal interface:

Welcome to Quiz Adventures — AI-Powered MCQ Game
1. Start New Session
2. View Performance Summary
3. View Performance Graph
4. Exit

---

5. Modular Architecture

Organized into clean files:

src/
 ├── main.py              # Entry point
 ├── mcq_generator.py     # OpenAI MCQ generator
 ├── performance_tracker.py
 ├── adaptive_engine.py    # ML difficulty engine
 └── data/
        training_data.csv

---

Installation

1. Clone the repository

git clone https://github.com/yourusername/adaptive-learning.git
cd adaptive-learning

2. Create Virtual Environment

python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

---

Environment Variables

Create a .env file:

OPENAI_API_KEY=your_api_key_here

---

Run the Game

python src/main.py

---

Example MCQ JSON Output

{
  "question": "What is the chemical symbol for water?",
  "choices": ["H2O", "O2", "CO2", "NaCl"],
  "answer_index": 0,
  "explanation": "Water consists of two hydrogen atoms bonded to one oxygen atom.",
  "difficulty_score": 2
}

---

Future Improvements

• Add memory-based reinforcement learning
• Introduce user profiles & progress graphs
• Export progress reports
• Add web UI (FastAPI + React/Streamlit)
• Add leaderboard & gamification

---

Contributing

Open for contributions! Suggestions, PRs, improvements — all welcome.

---

License

MIT License.

---