Three Learning Paths in the Age of AI: An Educational Philosophy under Limited Interdisciplinarity

This work proposes a structural educational-philosophical framework for human learning in the age of artificial intelligence (AI), grounded in the premise that interdisciplinary research—while present—remains institutionally constrained and grows only weakly. Because contemporary AI systems inherit the fragmented architecture of existing academic disciplines, they do not autonomously question or reconstruct disciplinary boundaries. Under these conditions, human learners retain distinctive roles that cannot be delegated to AI. The paper identifies three learning paths as structured options for human development: (1) Depth, defined as the pursuit of unexplored or under-theorized domains; (2) Breadth-with-depth (Interdisciplinary Integration), defined as boundary work that connects fragmented fields through conceptual and methodological synthesis; (3) Novel Breadth, defined as the discovery and articulation of new domains or problem spaces beyond existing disciplinary maps. Rather than prescribing a single ideal mode of learning, the framework positions these three paths as choices that become especially salient given the structural limitations of AI and the institutional constraints on interdisciplinarity. The paper argues that educational philosophy in the AI era must shift from content-centered models toward enabling learners to understand and select among these paths. This approach provides a conceptual foundation for rethinking curricula, assessment, and institutional design in higher education.