Feynman buddy

Learn better by explaining smarter.

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Inspiration

I've always done passive learning by reading out topics to myself or friends and it helped me understand more. But I always wanted someone who could tell me where I am wrong and were to pute more effort. And being honest, not everybody has someone who they can tell all they know about some topic which doesn't concern the other person. Finding out that passsive learning is a well-documented method, I wanted to develop something that listens to your explanation and provides accurate feedback—pointing out where you're right, what you got wrong, and what requires more elaboration. And so the idea of Feynman Buddy was established.

What it does

Feynman Buddy allows users to describe any subject in their own words, compares it to Wikipedia's summary, and gives precise feedback highlighting correct points, missed points, and areas requiring elaboration to assist better understanding.

How we built it

I employed Python and Flask in the backend, implemented the Wikipedia API to retrieve summaries, and utilized text similarity algorithms to match user input against Wikipedia content. The frontend is minimal and user-friendly, and I provided speech-to-text support for smoother input.

Challenges we encountered

Comparing explanations with various wording but identical meaning was challenging. I adjusted similarity thresholds to strike a balance between accuracy. Incorporating speech recognition across browsers involved additional effort, and dealing with ambiguous Wikipedia pages involved solid error handling.

Accomplishments that we're proud of

I created a functional app that provides significant, actionable feedback in real time. The speech-to-text functionality improves usability and accessibility, making learning more engaging.

What we learned

I gained experience working with natural language processing, web development, API integration, and designing user-driven feedback systems.

What's next for Feynman Buddy

I will enhance semantic comprehension with the use of advanced NLP, implement multilingual support, improve speech recognition, and implement quizzes and progress tracking for enhanced learning engagement.

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