Changes at "AI Shadow Classroom : Silent background monitoring that detects student confusion and to personalize learning."

NeuralBits

Sankalp Dangi,Saniya Khatoon, Sakshi Budholiya(IPS academy)

Training / education / pedagogy

We are building an AI learning platform that works like a personal tutor inside a video player. It creates real-time

+notes and learns each student's study style. By tracking behavior like repeated rewinds, it detects when someone is

+stuck — pausing to explain concepts in a simpler way. It supports multiple regional Indian languages via Bhashini

+API. If a class is missed, it generates instant AI summaries to catch students up. Built for students who never had

+access to private tutoring.

Every classroom has students who are completely lost but will never raise their hand. Maybe the explanation was too

+fast, or maybe English isn't their first language. Whatever the reason, they fall behind — and nobody notices. This

+keeps happening until they give up. We've seen it. We've felt it. The tools that exist today are often made for

+students who already have advantages: good devices, strong English, and paid coaching. Our platform levels the

+playing field. It detects confusion before it becomes failure, explains things in the language the student is

+comfortable in, and makes sure a missed class never turns into a missed semester.

We are three CS students from Indore. We didn't come from big cities or fancy colleges. We've sat in classrooms

+where the lecture moved on while half the room was still stuck on the previous slide. Nobody said anything. That's

+the problem we want to fix. We joined this challenge because we want to build something that actually helps

+students like us — not just students who already have every advantage. We want to prove that a small team from a

+Tier-2 city can build something worth showing to the world.

We are building a web platform using React, FastAPI, SQLAlchemy and SQL.

+We have a custom HTML5 video player built in React that tracks every rewind, pause and skip a student makes during a lecture. This data gets sent to our FastAPI backend and stored in our SQL database. When a student rewinds the same clip three times, the system automatically pauses the video and generates a simpler explanation for that exact moment using an LLM.

+For notes, we run the lecture audio through Whisper to get a full transcript. That transcript goes into an LLM which structures it into clean readable notes. Every time a student edits their notes, we save those changes in the database against their profile. The system analyzes their editing pattern and the next time they open a lecture, their notes already come formatted in their preferred style.

+All notes, summaries and explanations pass through the Bhashini API which translates everything into the student's chosen regional language in real time. Students can switch languages anytime from their dashboard.

+After each lecture ends, our backend pulls that student's confusion data — which moments they rewound, which sections they spent most time on — and passes it to an LLM to generate a personalized quiz targeting exactly those weak points.

+If a student missed a class, Whisper transcribes the full lecture and the LLM generates a structured summary with key points and questions so they can catch up before the next session.

+All student data, notes, quiz results and watch history are stored and managed through SQLAlchemy.