PedaL

Inspiration

We believe in the principle of "you can always better your best." Just as students improve through studying and proactive learning, teachers can enhance their teaching methods. PedaL aims to further the cause of education by enabling teachers to evaluate their teaching comprehensiveness, effectiveness, and relevance. This tool is valuable for emerging tutors, middle school teachers, and university professors alike.

What it does

PedaL is an innovative pedagogy tool that provides teachers/tutors with feedback on their teaching performance. Leveraging Hume AI for emotion recognition and Intel IPEX LLM for advanced analysis, PedaL enhances educational outcomes through detailed behavioral and technical feedback.

How we built it

Frontend: Tailwind, TypeScript, NextJS Backend: Flask LLM: OpenAI ChatGPT

Challenges we ran into

We initially opted to use the Intel IPEX LLM with the Intel Tiber Developer Cloud but faced difficulties connecting the Cloud Development Environment to the codebase. SSH issues into the cloud posed the biggest challenge.

Accomplishments that we're proud of

We successfully implemented emotion recognition and ranked them for teaching effectiveness with a model that displays these metrics.

What we learned

Integrating LLMs expands the usage of a web app to offer a more personalized and nuanced experience.

What's next for PedaL

We plan to integrate an LLM whose knowledge is based solely on what the teacher taught, referred to as "Student." This LLM will be quizzed by a master LLM, generating metrics of understanding and evaluating the effectiveness of the teaching.