Meritocracy and generative AI in higher education: institutional pathways and use–response alignment across four systems

This study examines how higher education systems respond to the rapid adoption of generative artificial intelligence (GenAI) under different institutional conditions, asking how meritocratic competition is translated into assessment and credential pressure and how student GenAI use aligns with institutional responses. Using a comparative qualitative case-study design, the study analyzes secondary evidence from China, the United Kingdom, the United States, and Germany through two a priori frozen indicator frameworks. The first framework traces how meritocratic competition is institutionalized through admissions, grading, and credential differentiation; the second maps student GenAI use patterns against institutional responses in teaching, assessment, and support. Strong credential differentiation and education–labour-market linkage emerge in all four systems, but through distinct pathways: state-coordinated in China, marketized in the United Kingdom, decentralized and market-oriented in the United States, and state–professional in Germany. Partial use–response alignment is the cross-system norm; systems differ mainly in pedagogical integration, regulatory adaptation, and institutional support. AI literacy and support provision lag behind student adoption in all four cases, making institutional capacity the most persistent source of misalignment, while limited comparable measurement of equity alignment and of guarded and compensatory use shows that monitoring capacity is itself part of governance. GenAI governance therefore converges less on common rules than on common capability gaps. The findings suggest that governing GenAI in meritocratic systems depends less on uniform rules than on sustained investment in institutional monitoring and support capacity.