Maestro

Inspiration

As a team with experience in early childhood education and tutoring, we noticed a significant gap in the literacy levels with which students enter college. Given the use of high-level texts in many courses, we aimed to create a tool to assist students in developing these crucial skills by simplifying such texts.

What it does

Maestro accepts a link to any online article or research paper and simplifies it based on the user's selected difficulty level. It then presents the user with the simplified article, along with tailored questions at the end to assess their understanding and ensure comprehensive comprehension of the text.

How we built it

This software was built using Django for the backend functionality, BeautifulSoup for the web scrapers handling the original inputted text, Postgres for the article database, Bootstrap for the UI and front-end development, and OpenAI's API for the AI component.

Challenges we ran into

We faced challenges in properly integrating the API into the project and implementing safeguards to ensure consistent and accurate conversion of all articles. The team also devoted considerable time to problem generation, ensuring the relevance and matching of difficulty levels.

Accomplishments that we're proud of

We are proud to announce that the product currently functions as intended, and we firmly believe it can make a difference in students' academic journeys. Our hope is that this tool will aid students in enhancing their literacy skills and fostering a passion for reading. Furthermore, by utilizing real online articles, Maestro has the potential to significantly improve overall knowledge of current events.

What's next for Maestro

Our plan is to further develop Maestro and tailor it for K-12 education. We envision teachers using this tool to customize articles for each student in their class and adjust the generated problems accordingly. Through this approach, students can enhance their literacy skills at their own pace, while teachers gain insights into individual student performance through the accuracy of the generated questions.