Inspiration

I've always enjoyed building things, but between work and life, finding uninterrupted time to create is hard.

When this hackathon came up, I saw it as an opportunity to experiment, learn, and push myself against a real deadline.

I was also drawn to the idea that while there are countless AI tools that generate answers, very few focus on the Feynman Technique—the idea that if you can explain something simply, you truly understand it.

That became the inspiration behind Splain.

How We Built It

Built with:

Bolt
Google Gemini API
AI-powered study pack generation
AI answer evaluation and feedback

Splain turns notes into:

✓ Summaries
✓ Flashcards
✓ Explain-it-back questions
✓ Personalized feedback on understanding, accuracy, and completeness

Focus Mode encourages students to recall information from memory before receiving feedback.

Challenges

The biggest challenge wasn't technical—it was time.

Balancing life and committing consistent time to the project was difficult.

Early on, I spent too much time testing Gemini API calls in Python before simply integrating them directly into the application. Looking back, I probably over-engineered that step and lost valuable development time.

I also nearly abandoned the project after discovering TeachBack AI, a similar concept. I worried I was building something that already existed.

After trying it, I realized the experience wasn't what I envisioned. It was slow, and I couldn't get feedback functionality working consistently.

That experience reinforced something important: