graider

Inspiration

We realized that grading papers is time-consuming and repetitive, often lacking meaningful feedback for students. This inspired us to create a solution that automates grading and enhances student feedback.

What it does

Graider grades essays, assignments, and exams based on a provided rubric or one generated from the exam sheet. It also includes a feedback section for both students and graders, making the process more efficient and insightful.

How we built it

We built the platform entirely using Python, leveraging Reflex to develop both the frontend and backend seamlessly. We also utilized the Groq LLM library to handle the complexities of grading papers accurately and efficiently

Challenges we ran into

Working with Reflex, a new technology, was challenging. As we adapted to it, we faced a steep learning curve in scaling our project quickly using only our knowledge of Python.

Accomplishments that we're proud of

We’re proud of building an entire system using Python (thanks to Reflex). Additionally, we explored new tools provided by our sponsors—VAPI, Groq, and Fetch AI—which helped us enhance the project by integrating advanced AI capabilities.

What we learned

We gained a deep understanding of Reflex and how to build frontend applications entirely in Python. We also explored different LLM models, refining our approach to achieve optimal latency rates suitable for the project’s needs.

What's next for graider

We plan to add a chatbot feature that allows graders to adjust student grades via voice commands. Additionally, we aim to incorporate image scanning to grade handwritten papers automatically. Our goal is to scale Graider to educational institutions across the country.