Published Paper Details:

The challenge of providing personalized education in modern classrooms has become increasingly complex due to diverse learning needs, varying cognitive abilities, and the growing demand for individualized instruction. Traditional educational approaches--from one-size-fits-all textbooks to static video content and limited adaptive learning platforms--are proving inadequate in addressing the unique learning pace and comprehension levels of individual students. These limitations not only hinder academic progress but also contribute to student disengagement, knowledge gaps, and reduced learning outcomes, particularly in foundational subjects during critical developmental years. Recent research has focused on intelligent tutoring systems that leverage artificial intelligence (AI), natural language processing, and adaptive content generation to overcome these educational challenges. Among these innovations, AI-powered video generation systems, similar to Google's NotebookLM approach, have emerged as promising solutions that can create personalized educational content while maintaining engagement and comprehension through dynamic visual and auditory elements. This survey compiles and examines advancements in AI-driven personalized video tutoring systems, with emphasis on student modeling, adaptive content generation, and real-time assessment integration. We analyze existing works that incorporate large language models (LLMs) and video generation technologies into educational frameworks, evaluate their effectiveness compared to traditional and hybrid learning approaches, and highlight their potential to reduce learning gaps, improve comprehension rates, and enhance overall educational outcomes. The study also identifies unresolved challenges including content accuracy verification, scalability across diverse curricula, real-time processing requirements for interactive questioning, and adaptation to varying technological infrastructure in educational institutions. This work provides a structured perspective on how AI-powered video tutoring systems can evolve within broader educational technology frameworks by synthesizing insights from current research trends in personalized learning, student assessment, and adaptive content delivery. The survey aims to serve as a foundational reference for future research, bridging AI-driven educational content generation with practical classroom applications for K-12 education.