Zina | Devpost

Inspiration

Through meeting hundreds and hundreds of learners from all over the world in our time as university students, we have slowly realized over time the variability that exists in education systems around the world. After many conversations, it is apparent that in many areas, teenagers and young adults do not have anywhere near the quality of education that one may find at an accredited four-year university-- an experience that requires thousands of dollars to have. With such a boom for artificial intelligence and research in educational best practices, we came up with a way to allow for all those who yearn for this learning to have it.

As such, we sought to build an AI-powered tutoring with a human touch to create an engaging, community-based, learning experience. The goal is to help students, no matter where they are, to get real-time guidance through a platform that is not just instructional, but also one that is engaging and that pushes students to want to learn more. We believed that our combined experiences in machine learning and fullstack development was fit for the job.

What it does

Zina is a personalized group instructor that uses video and audio sessions to guide student learning. Students can generate course topics of their choice and be in a classroom with peers of their choice, putting the power of learning into the hands of the students.

Our application supports features such as lesson plan generation, live video streaming, questions and answers.

How we built it

Tools:

Next.js + React.js
Tailwind CSS
Flask
Socket.IO
WebRTC
Deepgram
Agora Video API
Chroma DB Multimodal Data
Langchain

Challenges we ran into

One of the biggest challenges was ensuring speech to text and text to speech communication is effective and engaging. Handling real-time communication across various network speeds was tricky, and balancing performance with AI processing for live transcription and analysis was another technical hurdle.

Integrating the Deepgram API and ensuring the seamless operation of AI services without lag was the main point of challenge here. Additionally, ensuring the platform worked equally well across different devices and operating systems required extensive testing and optimization.

Accomplishments that we're proud of

We are proud of the seamless integration between AI-powered transcription, real-time feedback, and live tutoring sessions. The combination of voice recognition and real-time analysis to help tutors adjust lessons based on students' needs was a significant accomplishment.

What we learned

The importance of scalability in real-time applications. Building for low latency while incorporating AI services that require heavy computational resources taught us how to optimize both frontend and backend processes in short time frames. We also gained insights into how students and tutors interact in virtual settings, helping us design a more intuitive user experience.

This project also reinforced our knowledge of WebRTC and how to leverage AI to enhance human interaction in education.

What's next for Zina

Next, we hope to expand on our philosophy of quality education by engaging in continuous research in education practices to ensure that students' needs are met. Due to the limited time constraints, we were forced to cut corners, which made us all the more eager to go the full lengths to make this project complete.