Inspiration
It started with a simple question: Why do students miss scholarships when so many opportunities are available?
While exploring the problem, we realized that students often have to search through many different websites, understand complicated eligibility criteria, prepare multiple documents, and keep track of different deadlines. Even after all this effort, they may still not know which scholarships actually match their profile.
We wanted to turn this confusing process into something simple and personalized. That idea led us to build ScholarMatch AI.
What it does
ScholarMatch AI is an AI-powered scholarship and financial-aid assistant that helps students discover scholarships that match their profile.
Students provide information such as their academic performance, course, financial situation, location, achievements, activities, and career interests.
The system then analyzes their profile and identifies relevant scholarship opportunities. More importantly, it explains why they match, which requirements they satisfy, and what documents or requirements are still missing.
It also helps students track scholarship deadlines and application progress.
How we built it
We built ScholarMatch AI using a combination of rule-based eligibility checking and AI-powered semantic matching.
First, the eligibility engine checks important conditions such as GPA, income, degree, study level, location, and other scholarship requirements.
Then, AI-based semantic matching helps understand less structured information such as career goals, interests, skills, extracurricular activities, and community service.
The results are presented through a simple dashboard showing scholarship matches, match percentages, deadlines, funding information, and application checklists.
Our technology stack includes Next.js, React, Python/FastAPI, PostgreSQL/Supabase, pgvector, NLP, embeddings, and LLM-based AI services.
Challenges we ran into
One of our biggest challenges was making the AI recommendations reliable.
We realized that important eligibility decisions should not depend completely on AI. So, we combined deterministic rules for critical requirements with AI for semantic understanding and explanations.
Another challenge was converting complicated scholarship criteria into information that students could easily understand.
We solved this by creating an explainable recommendation system that clearly shows what matches, what is missing, and what the student needs to do next.
Accomplishments that we're proud of
We are proud that we built more than just a scholarship search system.
ScholarMatch AI connects the complete journey:
**Student Profile → Eligibility Check → AI Matching → Explanation → Scholarship Discovery → Deadline Tracking → Application Checklist
Our biggest accomplishment is making the recommendations personalized and explainable, so students can understand not only which opportunities are relevant, but also why.
What we learned
This project taught us that solving a real-world problem with AI is not just about using an AI model.
We learned how to combine AI, databases, rule-based systems, semantic search, and user-friendly design into one working solution.
We also learned the importance of transparency when AI is used for education and financial information. AI should assist students with useful insights while clearly communicating uncertainty rather than making unsupported guarantees.
What's next for ScholarMatch AI
We want to take ScholarMatch AI beyond a prototype.
Our future plans include expanding the scholarship database, adding university integrations, AI-assisted application and essay support, document verification, and email/calendar deadline reminders.
We also want to make the platform available as a mobile application and improve personalized financial planning.
Our vision is simple:
No student should miss a potential scholarship simply because finding and understanding it was too complicated.
ScholarMatch AI — turning scholarship hunting into an intelligent, personalized, and explainable financial-aid journey.
Built With
- api
- css
- embeddings
- fastapi
- llm
- natural-language-processing
- pgvector
- postgresql
- python
- react.js
- rest
- search
- semantic
- supabase
- tailwind
- vercel
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