Quercus++

Inspiration

What it does

Quercus++, an addition addon to help with navigating the complexities of UofT's Quercus portal. Quercus++ aggregates students course data into a single intelligent interface. Students see a to-do list that keeps tabs on everything they need to do, a consolidated calendar of every deadline and exam across all courses (not just the ones that professors explicitly post on the Quercus calendar), and visualizations of their grades.

How we built it

Quercus++ is a full-stack web application built with React on the frontend and Node.js/Express on the backend. The frontend provides a unified dashboard for course data, to-do items, deadlines, calendar views, and grade summaries. The backend acts as a secure proxy for Canvas/Quercus API requests so we can avoid browser CORS issues while keeping the user’s token transient. We also designed the platform to support AI-powered assistance using AWS tooling, with the goal of turning raw course data into personalized academic guidance. For local development and deployment planning, we used a modern cloud-oriented stack including React, Express, PostgreSQL, and AWS services such as Bedrock, Amplify, Lightsail, and RDS.

Challenges we ran into

One major challenge was working with Quercus data itself. Course information is not always structured consistently, and important academic details like deadlines, grades, and announcements are spread across multiple API endpoints. Another issue was that direct browser requests to the Quercus API run into CORS restrictions, so we had to build a backend proxy layer to make the data accessible in a usable way. We also had to think carefully about caching, token handling, and how to present large amounts of academic information in a way that feels helpful instead of overwhelming.

Accomplishments that we're proud of

We are proud that Quercus++ turns a messy academic workflow into something clear and actionable. Instead of forcing students to click through each course one by one, the platform aggregates their responsibilities into one interface with a unified to-do list, consolidated calendar, and course-level summaries. We are also proud of the technical foundation we built: a clean frontend architecture, a backend proxy for Canvas data, and an extensible design that supports AI features and future AWS deployment. Most importantly, we created something directly relevant to student life that solves a real day-to-day problem.

What we learned

We learned that building student tools is not just about accessing data, but about translating that data into decisions. Raw LMS information is often noisy and incomplete, so thoughtful filtering, summarization, and UI design matter just as much as backend integration. We also learned a lot about full-stack architecture, API proxy design, environment configuration, and the practical tradeoffs involved in connecting an academic platform with AI and cloud infrastructure. This project reinforced how much value there is in combining software engineering with a very specific user problem.

What's next for Quercus++

Our next step is to make Quercus++ even more intelligent and personalized. We want to expand the AI assistant so it can answer student questions, summarize course expectations, and recommend what to focus on next based on deadlines and grades. We also want to improve calendar intelligence by detecting more assignment and exam events automatically, add stronger grade analytics and forecasting, and support long-term deployment through AWS so the platform can be used reliably by real students. Longer term, we see Quercus++ becoming a full academic copilot for Quercus users.