Athena

By: Ashley Chin, Kimberly Maynard

Athena is a full-stack AI-powered quiz generation web application that creates interactive quizzes based on any topic the user enters.

Instead of using static or pre-written questions, Athena dynamically pulls information from Wikipedia and uses AI to generate natural, high-quality questions, explanations, and adaptive difficulty.

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Features

• Topic-based quiz generation (e.g., AI, Machine Learning, Organic Chemistry)
• AI-enhanced question generation (not just basic scraping)
• Difficulty levels
  • Easy
  • Medium
  • Hard
• Multiple question types
  • Multiple Choice (A, B, C, D)
  • True / False (clickable like A/B)
  • Definition-based questions
• Timer system (auto-submit when time runs out)
• Progress bar (fills as you answer)
• Answer explanations shown after submission
• Retry / regenerate quiz functionality
• Full-stack architecture
  • Python (Flask backend)
  • HTML / CSS / JavaScript frontend

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Tech Stack

Frontend

• HTML5
• CSS3
• Vanilla JavaScript

Backend

• Python 3
• Flask
• NLTK (text processing)
• Scikit-learn (TF-IDF keyword extraction)
• Requests (Wikipedia API calls)

APIs

• Wikipedia API (content source)
• OpenAI API (AI-generated questions & explanations)

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Installation & Setup

1. Clone the Repository

git clone https://github.com/kimmaynard/athena.git
cd athena

2. Create Virtual Environment

Mac / Linux

python3 -m venv venv
source venv/bin/activate

Windows

python -m venv venv
venv\Scripts\activate

3. Install Dependencies

pip install flask nltk requests scikit-learn openai

4. Set OpenAI API Key

Mac / Linux

export OPENAI_API_KEY="your_api_key_here"

Windows (PowerShell)

setx OPENAI_API_KEY "your_api_key_here"

5. Run Backend Server

python app.py

You should see:

Running on http://127.0.0.1:5000/

6. Run Frontend

open index.html

Or use Live Server in VS Code.

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How to Use

1. Enter a topic (e.g., "AI")
2. Select difficulty (Easy / Medium / Hard)
3. Choose number of questions (3, 5, 10)
4. Click Start Quiz
5. Answer questions
6. Click Submit
7. View results + explanations

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Project Structure

athena/
│
├── app.py                  # Flask API server
├── athena_quiz_engine.py   # Core quiz logic
│
├── index.html              # Frontend UI
├── style.css               # Styling
├── script.js               # Frontend logic
│
├── venv/                   # Virtual environment
└── README.md

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Future Improvements

• User accounts
• Quiz history
• Leaderboard
• Better AI explanations
• Mobile optimization

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Project Responsibilities

Ashley Chin

• Implemented and integrated frontend–backend communication using Flask API endpoints
  • Connected to /api/quiz for quiz generation
  • Connected to /api/grade for grading and feedback
• Designed and managed the end-to-end quiz workflow:
  • User input → API request → rendering → submission → results
• Structured and handled JSON-based API requests and responses
• Built dynamic state management system for answers, progress tracking, and submission state
• Implemented timer system with auto-submit functionality
• Developed the interactive quiz interface, including:
  • Dynamic question rendering
  • Answer selection handling
  • Progress bar updates
• Debugged and resolved integration and functionality issues across frontend and API
• Designed and refined the user interface (UI/UX) for usability and consistency
• Authored and structured the project README documentation, including setup and usage instructions

Kimberly Maynard

• Developed the backend quiz generation engine using Python and Flask
• Implemented and structured API endpoints:
  • /api/quiz for quiz generation
  • /api/grade for grading logic
• Integrated Wikipedia API for dynamic content retrieval
• Implemented text processing pipeline using NLTK for sentence extraction and filtering
• Applied TF-IDF (Scikit-learn) for keyword extraction and ranking
• Built distractor generation logic for realistic answer options
• Implemented grading system with scoring and detailed results output
• Created the final project report and PowerPoint presentation documenting system design and implementation

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License

This project is for educational use.

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Final Thoughts

Athena transforms passive learning into an interactive AI-powered experience.

Users don’t just memorize — they engage, understand, and learn smarter.

Presentation Demo Youtube Link

https://youtu.be/0k9ZWB_4OvM

Sources

Bird, Steven, Edward Loper, and Ewan Klein. Natural Language Processing with Python. O’Reilly Media, 2009.

Brown, Tom B., et al. “Language Models are Few-Shot Learners.” Advances in Neural Information Processing Systems, 2020.

ChatGPT. “Responses to queries about AI quiz generation project.” OpenAI, 13 Apr. 2026, https://chat.openai.com/.

Grinberg, Miguel. Flask Web Development. O’Reilly Media, 2018.

Kahoot! AS. “Kahoot!” https://kahoot.com/.

“MediaWiki API.” Wikimedia Foundation, https://www.mediawiki.org/wiki/API:Main_page.

Manning, Christopher D., Prabhakar Raghavan, and Hinrich Schütze. Introduction to Information Retrieval. Cambridge University Press, 2008.

OpenAI. “OpenAI API Documentation.” OpenAI, 2026, https://platform.openai.com/docs.

Pedregosa, Fabian, et al. “Scikit-learn: Machine Learning in Python.” Journal of Machine Learning Research, vol. 12, 2011, pp. 2825–2830.

Quizlet, Inc. “Quizlet.” https://quizlet.com/.