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The integration of Generative Artificial Intelligence (GenAI) into education represents a major shift, bringing powerful new capabilities that redefine how learning environments function, how instructional methods are designed, and how students engage with their education. GenAI provides tools that enable personalized learning experiences, allowing educators to tailor their approaches to meet the unique needs of each student. It fosters student agency, giving learners more control over their own educational journeys, and supports adaptive learning models that adjust in real time to provide the most effective support for diverse educational needs. As institutions across the world increasingly turn to AI-driven solutions, the transformative power of GenAI in education continues to grow. As editors of this volume, we recognized a growing need for a comprehensive, interdisciplinary collection that not only showcases innovative applications of generative AI in education, but also addresses the deeper pedagogical, technical, and ethical dimensions of this transformation. Our goal was to bring together global voices, diverse perspectives, and emerging research to capture both the promise and the complexity of integrating GenAI into teaching and learning. The resulting volume is a curated set of 21 chapters authored by leading experts and researchers from different educational systems, academic disciplines, and cultural contexts. This book, Generative AI in Education: Theories, Applications, and Ethical Frontiers, explores the far-reaching impact of these advancements. It gathers insights from leading experts in artificial intelligence, education, and technology, examining both the theoretical foundations and practical applications of GenAI in educational contexts. From the integration of AI in classroom instruction to its role in immersive learning environments and its potential to enhance assessment and feedback processes, this volume offers a comprehensive look at how GenAI is reshaping the educational landscape. The book also addresses the critical ethical considerations involved in the adoption of AI in education, including issues of bias, data privacy, transparency, and fairness. With AI systems playing a growing role in educational decision-making, ensuring responsible use is essential for maintaining trust and equity. By examining both the benefits and challenges associated with GenAI, this book provides guidance on how educational institutions can navigate the ethical complexities of AI adoption while maximizing its potential to improve learning outcomes. Through a blend of theoretical insights, empirical studies, and real-world case examples, this book equips educators, researchers, and policymakers with the knowledge needed to make informed decisions about integrating GenAI into educational practices. As the role of AI in education continues to expand, we hope that this collection will serve as a valuable resource, sparking further exploration and encouraging thoughtful dialogue on how to leverage GenAI to create more inclusive, effective, and adaptive learning environments. The chapters presented in this volume collectively highlight several key advancements in the field. First, they showcase how GenAI is being used to personalize learning experiences, automate complex instructional tasks, and support the design of more dynamic, multimodal curricula. Second, the volume identifies a growing emphasis on developing AI literacy for both teachers and students, recognizing that effective AI integration requires not only technical capacity but also critical awareness and pedagogical innovation. Third, the book surfaces important ethical challenges— particularly in relation to algorithmic bias, data governance, and the need for transparency in AI-assisted educational decision-making. As we look ahead, the future of GenAI in education appears simultaneously exciting and uncertain. The tools are evolving rapidly, and with them, new pedagogical possibilities and policy questions are emerging. Future directions include the refinement of AI-supported assessment systems, greater integration of multimodal AI into immersive learning environments, and the expansion of culturally responsive AI systems that support diversity and inclusion. This volume concludes with a call for continued cross-sector collaboration among educators, technologists, ethicists, and policymakers to ensure that the use of GenAI in education is not only innovative but also equitable, transparent, and human-centered. We invite readers to approach this book not only as a collection of academic contributions, but also as a living dialogue—one that will evolve as GenAI continues to shape the educational horizon.