Generative AI for Education: A Study of Text-to-Video Generation for Personalized Learning

Text-to-Video (T2V) generation, spurred by new progress in Artificial Intelligence (AI), offers a path for changing customized learning when it creates automatic teaching video material. In this paper, key advancements in T2V models are examined, including Generative Adversarial Networks (GANs), diffusion models, and transformer-based architectures, as well as their potential impact on modern educational practices. AI-generated videos can enable fully tailored instruction, along with improved accessibility, reduced production costs, and support for large-scale online learning. Ethical considerations, as well as content quality, algorithmic bias, plus pedagogical alignment, remain as challenges, particularly. This work explores several implications of T2V technologies in inclusive education and adaptive educational settings, as open research questions and future research directions are also identified. We must address each of these issues. The proper integration of generative video technologies in educational contexts does depend on that.