About the Project

ScholarTrack was inspired by a simple but frustrating reality: students often miss out on valuable opportunities not because they lack ability, but because information is scattered across too many places. Scholarships, competitions, internships, workshops, and exchange programs are usually posted on different platforms, each with its own deadlines and requirements. Keeping track of everything becomes overwhelming, especially for students managing schoolwork at the same time.

I wanted to build something that reduces this friction and helps students focus on what matters: discovering opportunities and actually taking action on them.

ScholarTrack is an AI-powered opportunity discovery and tracking platform. It allows students to search for relevant opportunities based on their interests and background, save them into a personal dashboard, and track their application progress over time. Instead of treating opportunities as isolated items, ScholarTrack organizes them into a structured journey from discovery to application.

How I Built It

The project is built using Next.js, TypeScript, and Tailwind CSS for the frontend, with Supabase handling authentication and database management. Google OAuth is used to provide a simple and fast login experience.

For opportunity discovery, I integrated an AI-based search flow using the Gemini API, which generates structured opportunity suggestions based on user queries. These results are then displayed as interactive cards where users can save items into their personal workspace.

The backend is implemented using Next.js server actions and API routes, ensuring a smooth full-stack experience without the need for a separate backend service.

What I Learned

This project helped me better understand:

• Full-stack application architecture with Next.js
• Authentication flows using OAuth and Supabase
• Designing user-centric product flows instead of just CRUD systems
• Structuring AI outputs into reliable, usable data formats
• The importance of balancing functionality, performance, and user experience

Challenges Faced

One of the main challenges was ensuring the AI-generated opportunity data remained structured and consistent. Since AI outputs can vary, I had to design validation layers to ensure the data could reliably fit into the application.

Another challenge was keeping the user experience simple while still supporting powerful functionality like AI search, filtering, and tracking. I had to avoid turning the interface into a complex dashboard and instead focus on a clean discovery-first experience.

Finally, integrating authentication and ensuring secure handling of user data required careful configuration between Supabase and the frontend.

Closing

ScholarTrack is still evolving, but the goal remains the same: helping students discover opportunities they might otherwise miss and giving them a clear way to track and act on them.

Built With

• css
• next.js
• routes
• shadcn/ui
• tailwind
• typescript