The emergence of generative AI models has catalyzed a paradigm shift in knowledge acquisition and learning. This paper holistically reviews the advancements and challenges in Large Language Models (LLMs) like GPT and their growing applications in education and training across diverse domains, including language, social science, health care, and science education. Central to these models are transformer architectures and self-attention mechanisms, enhancing these models’ ability to understand complex language patterns. While LLMs exhibit promise in creating lifelike training scenarios and aiding data-driven decision support, this review discusses domain-specific and general challenges in education. These challenges encompass crucial aspects such as data privacy and ethical considerations, quality and accuracy of generated content, assessment integrity, over-dependence and skill erosion, and implementation and accessibility. By detailing concrete use cases and examining opportunities along with limitations, this review aims to stimulate interdisciplinary discourse on effectively and responsibly incorporating AI advances into pedagogical practices to improve outcomes. The potential for human-AI collaboration and hybrid approaches are emphasized, rather than viewing such technologies as replacements for human teachers. We hope this analysis provides a balanced perspective across stakeholders to guide future research and integration efforts in this rapidly evolving landscape.