AI Annotated Recommendations in an Efficient Visual Learning Environment with Emphasis on YouTube (AI-EVL)

TL;DR

AI-EVL is an interactive visual learning system that enhances YouTube video learning by reducing bandwidth, preventing distraction, and enriching subtitles with AI annotations and ontological data for improved focus.

Contribution

The paper introduces AI-EVL, a novel system integrating AI-annotated, layered subtitles into online learning platforms to improve engagement and reduce bandwidth usage.

Findings

Significant reduction in bandwidth usage by filtering unwanted content.

Rich ontological data extracted from Google trend data.

Enhanced user focus through multi-layered, interactive subtitles.

Abstract

In this article, we create a system called AI-EVL. This is an annotated-based learning system. We extend AI to learning experience. If a user from the main YouTube page browses YouTube videos and a user from the AI-EVL system does the same, the amount of traffic used will be much less. It is due to ignoring unwanted contents which indicates a reduction in bandwidth usage too. This system is designed to be embedded with online learning tools and platforms to enrich their curriculum. In evaluating the system using Google 2020 trend data, we were able to extract rich ontological information for each data. Of the data collected, 34.86% belong to wolfram, 30.41% to DBpedia, and 34.73% to Wikipedia. The video subtitle information is displayed interactively and functionally to the user over time as the video is played. This effective visual learning system, due to the unique features, prevents the user's distraction and makes learning more focused. The information about the subtitle text is displayed in multiple layers including AI-annotated topics, Wikipedia/DBpedia, and Wolfram enriched texts via interactive and visual widgets.