Multidimensional responses to generative AI in higher education: an integrative framework linking institutional governance, teaching practice, and student psychological adaptation.

This research establishes and validates an integrative framework to examine the multi-layered responses of higher education to generative AI, focusing on the interplay between institutional governance, pedagogical practices, and student psychological adaptation. Using a student survey sample of 477 respondents across three disciplinary clusters and linking these data to institution- and course-level governance and implementation indicators, we estimate multilevel models and identify response profiles that differentiate adaptation outcomes. The findings indicate that the congruence between governance mandates and classroom execution, bolstered by robust implementation capacity, correlates significantly with enhanced student engagement, self-efficacy, and agency, while concurrently mitigating academic anxiety. Although evidence-based integrity controls facilitate the internalization of ethical norms, they may inadvertently heighten student anxiety in the absence of explicit regulatory frameworks and accessible institutional support. Latent response profiles further reveal that a conditional-aligned, high-support configuration yields the most favorable adaptation pattern, whereas low-clarity and low-support environments correspond to reduced positive adaptation and heightened anxiety. Equity analyses indicate meaningful heterogeneity across institutional tiers and disciplinary contexts, underscoring that effective and fair GenAI governance depends on operational clarity, verification-ready assessment design, and resource-backed support.