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Basic frontend setup
Inspiration
As high schoolers, we've all experienced the dread of missing a key lecture in AP Physics or Calculus — that sinking feeling of walking into class the next day completely lost. At times, even the lengthy lectures made it difficult for us to learn the material. That shared experience inspired us to create Parallel Professor — an AI-powered platform that ensures no student is left behind, even if they do end up missing class or have socioeconomic barriers that prevent them from attending lectures. Our team hopes to give everyone an equal and fair opportunity to learn, no matter their race, class, or gender.
What it does
Parallel Professor allows teachers and professors to upload recorded lectures in MP4 format, which are then transcribed and summarized using AI. The platform also includes a chatbot powered by OpenAI that can answer students’ questions based specifically on the content of the uploaded lecture. It’s like having a parallel version of your professor ready to help anytime — making sure learning never stops for anyone. It also allows the students to learn at their own pace.
How we built it
We built the frontend using TypeScript, Next.js, and Bootstrap for styling. We handled user authentication and data storage with Firebase. Video files are uploaded to AWS S3 using the AWS SDK, and those links are stored in Firebase to be accessed later. We incorporated Google Speech-to-Text to transcribe the videos, and used the OpenAI API to create a chatbot that can answer lecture-specific questions. We also implemented access codes for private videos and designed an intuitive dashboard for students and educators.
Challenges we ran into
We ran into several hurdles during development, including navigating AWS S3 permissions, deploying with Vercel, managing data storage in Firebase, figuring out how to get clean transcriptions with Google’s Speech-to-Text API, and integrating OpenAI’s API in a way that maintained lecture specificity.
Accomplishments that we're proud of
We’re proud of the aesthetic and responsive UI we developed — especially the math-inspired SVG backgrounds on the home and about pages with the limited 24 hours. We’re also proud of how we brought together multiple complex backend systems, including AWS, Firebase, and several APIs, into one seamless platform. Though we struggled a lot, we felt proud that we got the video-to-speech and speech-to-text API working.
What we learned
We learned the importance of time management — especially that taking hour-long Netflix breaks isn’t the best strategy! More seriously, we gained experience working with cloud storage, authentication systems, and AI integrations, and learned how to troubleshoot and problem-solve in a time-constrained environment. We also learned to split up work well, having members work on the frontend and others on the backend.
What's next for Parallel Professor
Next, we plan to pilot Parallel Professor with real teachers and professors. We’ll collect feedback to refine the platform and evaluate its effectiveness in real classroom settings. Our goal is to make sure this tool becomes a valuable resource for both educators and students around the world, no matter their background.
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