This chapter examines the integration of generative AI in education. This goes beyond the broad themes of challenges and opportunities presented in the emerging literature. We consider the issues of generative AI integration in more detail, suggesting new directions for research and development and outlining an understanding of generative AI’s educational affordances based on a refined interdisciplinary understanding of the technology. We argue here that the issues of integration can be understood in relation to generative AI literacy in education (GAILE) and generative AI in education (GAIED). GAILE establishes the necessary baseline for understanding generative AI, ensuring that educators, students, and other stakeholders possess a fundamental awareness of how AI technologies work, their capabilities, limitations, and ethical implications. GAIED takes this a step further by focusing on the practical application of generative AI in educational contexts. It provides the skills and strategies needed to implement these technologies effectively, enhancing the teaching and learning processes. This chapter proposes an approach to address these challenges through iterative, interdisciplinary, participatory experimentation in educational contexts, as well as a transdisciplinary approach to aggregate knowledge from small-scale experiments to address the issues and tensions in generative AI in education.