The "Huh?" Button: Improving Understanding in Educational Videos with Large Language Models

TL;DR

This paper introduces a novel feature for online educational videos that leverages large language models to enhance individual understanding by providing rephrased explanations, combining interactivity with face-to-face communication benefits.

Contribution

It presents a simple, scalable method to improve comprehension in educational videos using LLMs, including a prototype and insights on reducing carbon footprint through caching.

Findings

Prototype implementation demonstrates technical feasibility.

Rephrased explanations improve understanding.

Caching reduces environmental impact.

Abstract

We propose a simple way to use large language models (LLMs) in education. Specifically, our method aims to improve individual comprehension by adding a novel feature to online videos. We combine the low threshold for interactivity in digital experiences with the benefits of rephrased and elaborated explanations typical of face-to-face interactions, thereby supporting to close knowledge gaps at scale. To demonstrate the technical feasibility of our approach, we conducted a proof-of-concept experiment and implemented a prototype which is available for testing online. Through the use case, we also show how caching can be applied in LLM-powered applications to reduce their carbon footprint.