The rise of generative artificial intelligence (AI) holds profound implications for higher education worldwide. The challenges are not merely confined to ethical questions, academic integrity, or data privacy; they also extend into a need for long-term pedagogical transformation. In this presentation, I will forward the thesis that self-regulated learning is the linchpin for academic adaptability in this AI-driven landscape. Drawing on an expansive corpus of research, I will advocate for a shift in educational focus—from a traditional emphasis on measurable outcomes to an approach that values the nuanced processes of learning. The skill of evaluative judgement, or the ability to autonomously assess and adapt one's learning path, emerges as particularly crucial in this context. As we move towards an educational paradigm increasingly mediated by AI technologies, self-regulated learning becomes an indispensable asset. It empowers students and educators alike to navigate a complex and evolving academic environment. For stakeholders ranging from students to policymakers, I will offer a roadmap for cultivating resilient, adaptive learners in a world where the role of AI in education is ascendant. This perspective serves as both a call to action and a foundational framework for those engaged in shaping the future of global higher education.