AlgoWhiz is an AI-powered chatbot developed to assist learners in mastering computer science algorithms. Created as part of the Senior Capstone Project (CS 4366), AlgoWhiz integrates educational content, natural language processing (NLP), and interactive features to provide personalized support on a variety of algorithms, from sorting and searching to graph theory. Designed for students and educators, it simplifies complex concepts, offering code snippets, step-by-step guidance, and intuitive explanations for a user-friendly learning experience.
AlgoWhiz combines Python, Flask, and OpenAI’s GPT API to deliver dynamic responses and code examples in real time. The system leverages natural language processing (NLP) to interpret complex queries, while Voiceflow powers a seamless conversational interface. An SQL database supports efficient data retrieval, making AlgoWhiz a reliable, responsive tool tailored for mastering algorithms.
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
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