Abstract: Generative artificial intelligence (AI) holds substantial promise for transforming education by enabling personalized learning experiences, automating content generation, and enhancing educational outcomes. This study reviews the current landscape and future outlook of generative AI in education, exploring its applications, benefits, challenges, ethical considerations, and future research directions. The review highlights diverse applications of generative AI, including personalized tutoring systems, adaptive learning environments, and data-driven educational analytics. These applications have shown potential in improving student engagement, learning efficacy, and instructional efficiency. However, the implementation of generative AI in education presents challenges such as algorithmic bias, transparency in decision-making, and ethical implications related to data privacy and technology dependence. Future research should focus on developing transparent and interpretable AI models, mitigating biases, ensuring data privacy, and assessing the long-term educational impacts of AI. Collaborative efforts across education, AI development, ethics, and policy-making are crucial to harnessing AI's potential responsibly and equitably in educational settings. This study advocates for informed decision-making and ethical considerations to maximize the benefits of generative AI while addressing its inherent challenges, ensuring a sustainable integration into educational practices. Keywords: Generative artificial intelligence, Education, Personalized learning, Adaptive learning environments, Educational analytics