This working paper proposes a conceptual framework for redesigning education in the AI age by shifting from knowledge storage to knowledge navigation. It argues that modern knowledge has expanded beyond the realistic memorization capacity of individuals, while artificial intelligence and digital systems increasingly store, retrieve, compare, and update detailed information more effectively than human memory. The paper suggests that education should therefore focus less on training humans as containers of disciplinary content and more on developing their ability to understand, evaluate, connect, and transform knowledge. The model introduces “informational structures” as recurring forms of reasoning that appear across disciplines, such as causality, systems, evidence, risk, uncertainty, human behavior, values, communication, design, and computation. It proposes that schools should teach these structures as universal foundations for understanding reality, while universities should offer advanced specialization pathways based on transferable informational structures applied across domains such as law, medicine, economics, public policy, AI, environment, business, and security. Traditional disciplines are not rejected in this model. Instead, they are repositioned as application domains, case libraries, professional traditions, and testing environments where deeper structures of reasoning can be practiced. The paper also discusses the role of AI as memory infrastructure, the importance of human judgment and responsibility, the risks of over-abstraction and AI dependency, and a gradual roadmap for implementation through school modules, university minors, interdisciplinary degrees, AI-supported knowledge libraries, and redesigned professional certification. The central claim is that the future of education should not ask humans to memorize civilization, but to understand, guide, and improve it.