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

Abstract: 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

## introduction the landscape of computer science education is rapidly evolving, with instructional videos becoming an increasingly integral part of the learning process. recent surveys indicate that over 78% of computer science students frequently turn to youtube as a supplementary resource to their textbooks and classroom instruction. this trend underscores a growing demand for high-quality, accessible video content in educational settings. however, the abundance of online resources presents its own challenges. the task of finding appropriate and reliable content can be overwhelming for students, leading to inefficient learning experiences. this difficulty has resulted in a significant preference shift, with 73% of students expressing a desire for curated video libraries that are tailored to their specific course requirements. in response to these challenges and preferences, we propose a novel approach that harnesses the power of large language models (llms) and natural language processing (nlp) technologies to transform the landscape of educational video content. our research focuses on the intersection of these advanced ai technologies with instructional videos, particularly addressing the crucial aspect of generalization in nlp models within an educational context. this paper presents a framework for creating aienhanced learning experiences that go beyond traditional video playlists. by leveraging generative ai powered by advanced llms, we demonstrate how standard instructional videos can be transformed into interactive, personalized learning tools. our approach not only fosters active learning and personalized education but also serves as a testbed for evaluating the adaptability of llms across diverse computer science topics and varying student proficiency levels. ## methodology our research utilizes the cubits.ai platform, developed at princeton university, to demonstrate how generative ai can transform standard video playlists into interactive, ai-enhanced learning experiences. the methodology encompasses several key components: ## conclusion this research demonstrates the transformative potential of integrating llms and nlp technologies into educational video content. by leveraging these advanced ai capabilities, we can create more engaging, interactive, and personalized learning experiences for students. the cubits.ai platform serves as a proof of concept, showcasing how ai-enhanced videos can revolutionize computer science education. our findings not only contribute to the ongoing dialogue about generalization in nlp but also provide practical insights into the application of llms in educational settings. by bridging these domains, we have established a shared platform for state-of-the-art generalization testing in nlp within an educational framework. as we continue to refine these technologies and gather more data on their impact, we anticipate that ai-enhanced educational videos will play an increasingly crucial role in the future of higher education. 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. the scalable architecture of the cubits.ai platform, combined with its micro-payment structure, offers a flexible and accessible approach to high-quality learning. as more students turn to video content for knowledge acquisition, platforms like cubits.ai are poised to become integral components of modern digital education, making high-quality learning both affordable and accessible.