From Fragmented Adoption to Institutional Articulation: A Six-Axis Framework for Capability-Enhancing Generative AI in Higher Education

Generative artificial intelligence (GenAI) is being integrated rapidly across higher education, yet its educational value depends less on adoption intensity than on the institutional conditions through which its use is organized and enacted. This conceptual study develops a six-axis framework for examining institutional articulation across governance, curriculum, assessment, faculty development, digital ethics, and equity. Drawing on a selective integrative analysis and conceptual synthesis, the framework moves from a dimensional taxonomy of institutional interventions to a configurational account of their interdependence. It distinguishes institutional articulation, the process through which institutional functions are connected, interpreted, resourced, and revised, from institutional coherence, the variable degree of alignment that results. Human capability formation provides the substantive educational criterion for evaluating that coherence, distinguishing capability-enhancing configurations from arrangements oriented primarily toward efficiency, automation, or output production. The framework further situates the six institutional axes within a broader relational architecture in which affect, trust, and perceived legitimacy condition enactment; assessment validity and demonstrable learning provide evidence of capability formation; and credential credibility, professional adaptability, stakeholder trust, reputation, and legitimacy constitute potential system-level consequences. The framework is operationalized through phased institutional transformation and illustrative process, outcome, and alignment evidence, and generates four propositions for future empirical research.