AlgoWhiz is an AI-powered chatbot designed to assist learners in mastering computer science algorithms. Developed as part of the Senior Capstone Project (CS 4366), AlgoWhiz provides interactive explanations, code snippets, and guidance on a wide range of algorithms. This tool was created to bridge the gap in understanding complex algorithmic concepts, offering personalized support for students and educators alike.
AlgoWhiz is a cutting-edge educational tool that leverages AI to provide dynamic, customized learning experiences for computer science students. It covers a variety of algorithms, including sorting techniques, searching methods, and graph algorithms, all presented with detailed explanations and interactive examples. The system is designed to be accessible through a web interface, where users can ask questions, receive immediate responses, and engage with algorithmic content in an intuitive and interactive way.
Python: The core programming language used for developing the backend logic.
Flask: The web framework used for building the backend server.
OpenAI API: Powers the AI functionalities that generate intelligent responses.
Voiceflow: Manages the conversational flows and user interactions.
Carrd: Provides the user interface for interacting with the chatbot.
- Replit: Used for hosting the backend server and managing the development environment.
- Fork the project from Replit:
- Set up an OpenAI account and obtain the necessary API key.
- Run the Python code in Replit to initialize the backend server.
- Access the chatbot via the Carrd interface, and start interacting with AlgoWhiz.
- Launch the AlgoWhiz application via the provided URL.
- Interact with the chatbot by typing your algorithm-related questions.
- Receive instant feedback, code snippets, and explanations.
This project was collaboratively developed by:
- Dhruv Maniar
- Isha Koregave
Developing AlgoWhiz provided deep insights into the intersection of AI and education:
- AI in Educational Tools: Applied AI to create an interactive, learning-driven chatbot, tailoring responses to enhance user engagement and provide educational support.
- Technology Integration: Gained experience integrating key technologies like Flask, Voiceflow, and OpenAI, building a seamless and responsive conversational AI system.
- User Interaction Design: Focused on creating an intuitive user interface, enhancing user interactions and ensuring a smooth, engaging experience for learners.
- Conversational AI Management: Developed expertise in managing complex conversational AI flows, optimizing the chatbot's ability to adapt to varying user inputs and educational needs.
This project solidified my skills in building scalable AI-driven applications for educational environments.
- Replit Project: AlgoWhiz on Replit
- Website: AlgoWhiz Website
Watch Video:
