Inspiration

Scholar is our one-stop platform designed to alleviate the challenges faced by college students in finding scholarships. With countless scholarship opportunities scattered across various sources, students often struggle to discover relevant funding options. Scholar streamlines this process by aggregating personalized scholarship opportunities based on a student's unique profile—making the journey to secure financial aid smoother and more efficient.

What it does

Scholar consolidates scholarship data from multiple sources into a single, easy-to-use platform. By analyzing a student’s resume, transcript, and other personal details, our intelligent matching system curates a list of scholarship opportunities that are best suited to their academic and personal profile. Whether a student is looking for merit-based awards, need-based aid, or opportunities for specific demographics, Scholar presents a comprehensive list—ensuring no opportunity is overlooked.

How we built it

Backend: We built our backend using Node.js, Express, and MongoDB. User authentication is handled with JWT, and user profiles are securely stored in the database. We integrated OpenAI’s GPT-3.5 to generate scholarship recommendations dynamically based on the user's data, and we use Puppeteer to scrape additional details like direct application links when needed.

Frontend: The front end is developed in React and styled with TailwindCSS and DaisyUI. The design focuses on clarity and ease of use, allowing students to search, filter, and view scholarships that match their criteria.

Data Integration: By combining AI-driven recommendations with live data scraping, Scholar delivers personalized, up-to-date scholarship opportunities, transforming the chaotic search process into a seamless experience.

Challenges we ran into

Connecting the backend to the frontend and getting the right data from the OpenAI to show up on our frontend. A lot of the time, it would use our fallback data instead of the actual recommendations from the AI. For some reason, it also wouldn't show all of the recommended scholarships, and it took us a long time to fix it.

Accomplishments that we're proud of

Fixing our problems and making an end-to-end website is something we are proud to say. We believe that it brings new information to users that they had not previously seen and that it can benefit college students.

What we learned

How can we debug and use different APIs and data features to coordinate together to solve a problem efficiently and benefit society?

What's next for Scholar

Plan on making essay graders and prediction outcomes so that users can rank the scholarships they want to target first.

Built With

  • mern
  • openai
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