In an era where generative AI is revolutionizing education, it is crucial for educators and students to look beyond algorithms to understand the physical materials that make this digital transformation possible. Generative AI technologies rely on critical minerals such as cobalt, lithium, and copper. These materials, essential for powering data centers, computing infrastructure, and hardware essential for AI to operate, are often extracted through exploitative labor conditions and cause ecological harm. While current conversations about responsible AI integration in education frequently center on data privacy, academic integrity, and equitable student access, a truly comprehensive approach must also address the environmental implications of AI’s material foundation. This session will explore the hidden costs and critical mineral dependencies that sustain generative AI systems, and it will challenge educators to consider how material extraction, global inequalities, and environmental justice intersect with AI deployment in classrooms. By integrating these perspectives into teaching, educators can play a pivotal role in expanding students’ critical digital literacy. By highlighting how the material extraction necessary for AI to function, they will be able to grasp the full lifecycle of AI tools beyond algorithms and outputs and the broader social and ecological contexts in which they operate. This interactive session will illuminate the often-overlooked environmental and ethical dimensions of AI and invite participants to reflect on how these issues can be woven into curriculum. The session will conclude with a participatory Q&A and discussion, offering attendees space to share ideas, raise questions, and explore strategies for fostering more responsible and sustainable AI integration in education.