EduGrant AI

Inspiration

We were inspired by the real challenges students face in accessing scholarships—so many deserving students fall through the cracks because the process is overwhelming. We took inspiration from both scholarship portals and personalized recommendation platforms, but we wanted to bring AI into the mix in a more human way.

What it does

EduGrant AI matches students with scholarships tailored to their profile. After students input their academic, financial, and personal details, the AI analyzes eligibility, approval likelihood, and funding gaps, then ranks the best matches. It not only suggests scholarships but also helps students track deadlines, build stronger applications, and plan their financial strategy.

How we built it

We used Lovable to create the user interface, ensuring a clean and responsive layout. The AI matching logic is powered by a simple rule-based system we built in Python, which scores students based on income, academic performance, and category eligibility. The front-end fetches and displays results dynamically, and we built a simple prototype using a sample scholarship dataset.

Challenges we ran into

One big challenge was ensuring the AI logic felt personalized without being overly complicated. We also had to balance a smooth UI with a realistic data model, since we didn’t have access to a large real-world scholarship dataset. Finally, we had to keep the design intentional and avoid looking like a generic AI template, which required extra attention to spacing and micro-interactions.

Accomplishments that we're proud of

We’re really proud that we built a prototype in just a few days that actually guides students step-by-step. The approval probability feature stood out, giving students a realistic sense of how likely they are to succeed. The dashboard design also gave us a chance to create a clean, human-centered experience.

What we learned

We learned how important balance is between AI and human design. The best AI tools guide users but don’t overwhelm them with jargon or complexity. We also learned a lot about the importance of micro-interactions—they give the site personality and keep users engaged.

What's next for EduGrant AI

Next, we want to expand the AI model by incorporating more data—real scholarship datasets and success stories—to fine-tune recommendations. We also plan to add a mobile app version, so students can track their applications on the go. Finally, we hope to partner with educational NGOs and local governments to scale this impact nationally.

Built With

• lovable
• react
• tailwand
• typescript
• vite