Active learning is AI that learns from your corrections, not just your data. Every time you press "done" or "more time" on Beacon, you're teaching Kairo's model what "AP Bio homework" actually takes for you specifically. Unlike passive ML systems that need thousands of examples before improving, active learning asks for your input on the predictions it's least confident about. This means Kairo gets accurate way faster than traditional productivity trackers.
The system works by maintaining an uncertainty score for each prediction. When Kairo predicts you'll finish a task in 45 minutes but its confidence is low, Beacon will pay extra attention to whether you actually finished in that timeframe. Your feedback gets weighted higher than similar historical data, so the model adapts to changes in your workflow—like when finals week hits and everything takes twice as long.