AI literacy as a meta-skill: a four-domain model for academic management innovation in higher education

As artificial intelligence (AI) becomes embedded in higher education operations, AI literacy is increasingly positioned as a meta-skill enabling institutional innovation; however, its contribution to academic management innovation remains underexamined in China’s private higher education sector. This study surveyed faculty and administrative staff from private institutions in Sichuan Province using a validated four-domain AI literacy (AILit) model—Engaging, Creating, Designing, and Managing—and tested its measurement and structural properties. Confirmatory factor analysis supported strong construct validity and reliability (Cronbach’s α = 0.86–0.93). Structural equation modeling indicated that all four AILit domains significantly predicted innovation outcomes ( p 0.001), with Managing AI showing the largest effect. The model demonstrated excellent global fit (CFI 0.95, TLI 0.94, RMSEA 0.05) and measurement invariance across academic versus administrative roles. The findings suggest AI literacy functions as a strategic, transferable capability extending beyond technical use to include governance, ethical oversight, and institutional alignment, underscoring the need for AI governance training and ethics-based implementation mechanisms. Limitations include the cross-sectional design, self-reported measures, and geographically bounded sampling; future work should use longitudinal, multi-source designs to strengthen causal inference and generalizability.