LLMs and NLP for Generalized Learning in AI-Enhanced Educational Videos and Powering Curated Videos with Generative Intelligence (2024)

LLMs and NLP for Generalized Learning in AI-Enhanced Educational Videos and Powering Curated Videos with Generative IntelligenceAuthors - Naina Chaturvedi, Rutgers UniversityAnanda Gunawardena, Rutgers UniversityContact: cnaina1601@gmail.com or nc832@cs.rutgers.eduThe rapid advancement of Large Language Models (LLMs) and Natural Language Processing (NLP) 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, all while considering the challenges posed by LLMs trained on vast, often opaque datasets. 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.Drawing insights from computer science courses at Princeton and Rutgers Universities, we highlight the transformative potential of AI-enhanced videos in promoting active learning, particularly in large classes. This research contributes to the ongoing dialogue about generalization in NLP while simultaneously demonstrating practical applications in educational technology. By bridging these domains, we aim to establish a shared platform for state-of-the-art generalization testing in NLP within an educational framework.Our findings not only demonstrate how educators can enhance existing video playlists using AI but also provide insights into the challenges and opportunities of using LLMs in educational settings. This work serves as a cornerstone for catalyzing research on generalization in the NLP community, particularly focusing on the application and evaluation of LLMs in adaptive, personalized learning environments.Keywords: Instructional videos; AI-enhanced learning; Large Language Models (LLMs); Natural Language Processing (NLP); generalization in NLP; computer science education; cubits.ai platform; AI-generated content; interactive video experiences; video summarization; on-demand questions; personalized learning; active learning; data-driven insights; generative AI; educational technology; adaptive learning environments