Inspiration
CUNY students often struggle to make informed decisions about course selection and professor choices. We were inspired to create a solution that would provide data-driven insights to help students understand grade distributions and teaching patterns, making academic planning more transparent and accessible.
What it does
CUNY GradeLens is an AI-powered platform that analyzes grade distribution data to provide personalized insights on professors and courses. It features an intelligent chatbot for natural language queries, advanced search capabilities for course discovery, and comprehensive comparison tools for evaluating teaching performance across all CUNY campuses.
How we built it
We developed CUNY GradeLens using: • React for the frontend, creating an intuitive and responsive user interface • Express.js for the backend API with robust search and analysis endpoints • SQLite for storing and querying comprehensive grade distribution data • OpenAI's GPT-4 for intelligent conversational responses and data analysis • Tailwind CSS for modern, accessible design components • Chart.js for interactive data visualization of grade distributions
Accomplishments that we're proud of
• Successfully integrating AI technology to provide intelligent grade analysis and insights • Creating a user-friendly interface that makes complex grade data easily accessible • Developing a system that can handle natural language queries about professors and courses • Building comprehensive search and comparison tools for academic decision-making
Challenges we ran into
• Processing and normalizing large datasets of grade distribution information from multiple semesters • Implementing intelligent course and professor name matching with fuzzy search capabilities • Balancing AI-generated insights with raw data presentation for transparency • Creating a conversational interface that maintains context across follow-up questions • We were not able to figure out the parsing of Rate My Professor links to integrate external review data
What we learned
• The importance of data preprocessing and normalization for accurate analysis • How to effectively combine AI with traditional database technologies for educational insights • The complexities of designing intuitive interfaces for complex academic data • The value of context-aware conversational AI for educational applications
What's next for CUNY GradeLens
• Expanding coverage to all CUNY campuses with comprehensive grade data • Implementing user authentication for personalized chat experiences and search history • Developing advanced analytics features for trend analysis across semesters • Creating mobile apps for iOS and Android for easier access on-the-go • Partnering with CUNY administration to integrate official grade distribution updates • Adding course difficulty prediction and success rate forecasting features • Implementing Rate My Professor link parsing to integrate external student reviews and ratings
Log in or sign up for Devpost to join the conversation.