ORIGINAL RESEARCH article
Front. Educ.
Sec. Higher Education
This article is part of the Research TopicArtificial Intelligence and Leadership: Shaping the Future of Higher EducationView all articles
AI Literacy as a Meta-Skill: A Four-Domain Model for Academic Management Innovation in Higher Education
Provisionally accepted
- 1Chulalongkorn University, Bangkok, Thailand
- 2Payap University, Tha Sala, Thailand
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As artificial intelligence (AI) becomes increasingly integrated into higher education, AI literacy is emerging as a core meta-skill for institutional innovation. Although widely recognized as multidimensional, its role in driving academic management innovation remains underexplored— particularly within China's private higher education sector. This study surveyed faculty and staff across private institutions in Sichuan Province using a validated four-domain AI literacy (AILit) framework comprising Engagement, Creation, Design, and Management. Confirmatory factor analysis confirmed strong structural validity and internal consistency (α = .86 – .93), and structural equation modeling showed that all four domains significantly predicted innovation outcomes (p < .001), with Managing AI emerging as the strongest predictor. The overall model demonstrated excellent fit (CFI > .95, TLI > .94, RMSEA < .05) and measurement invariance across academic and administrative roles. These findings position AI literacy as a strategic, transferable capability— extending beyond technical skill to include governance, ethical oversight, and institutional alignment. Practical implications include the need for AI governance training and ethics-based implementation mechanisms. Limitations include the cross-sectional design, reliance on self-reported data, and a geographically bounded sample; future research should adopt longitudinal, multi-source approaches to enhance causal inference and generalizability.
Keywords: academic management innovation, AI literacy, AILit framework, higher education, Meta-skills, Structural Equation Modeling
Received: 27 Nov 2025; Accepted: 30 Jan 2026.
Copyright: © 2026 Che, Chaemchoy and Siribanpitak. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Chi Che
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