LLMs and NLP for Generalized Learning in AI-Enhanced Educational Videos and Powering Curated Videos with Generative Intelligence

Naina Chaturvedi

Abstract: The rapid advancement of Large Language Models and Natural Language Processing technologies has opened new frontiers in educational content creation and consumption. This paper explores the intersection of these technologies with instructional videos in computer science education, addressing the crucial aspect of generalization in NLP models within an educational context. With 78% of computer science students utilizing YouTube to supplement traditional learning materials, there’s a clear demand for high-quality video content. However, the challenge of finding appropriate resources has led 73% of students to prefer curated video libraries. We propose a novel approach that leverages LLMs and NLP techniques to revolutionize this space, focusing on the ability of these models to generalize across diverse educational content and contexts. Our research utilizes the cubits.ai platform, developed at Princeton University, to demonstrate how generative AI, powered by advanced LLMs, can transform standard video playlists into interactive, AI-enhanced learning experiences. We present a framework for creating AI-generated video summaries, on-demand questions, and in-depth topic explorations. Our approach not only enhances student engagement but also provides a unique opportunity to study how well these models generalize across different educational topics and student needs.