Abstract

Against the backdrop of China’s Education Modernization 2035 agenda, this chapter develops and empirically tests a collaborative framework in which human expertise and artificial intelligence jointly inform administrative decision-making across K–12 and higher-education contexts. Drawing on the Technology Acceptance Model and classical symbiosis theory, the study adopts a two-phase mixed-methods design that privileges qualitative insight. Phase one comprised semi-structured interviews with thirty administrators (fifteen from primary and secondary schools and fifteen from universities) to surface perceptions of AI-augmented workflows, anticipated benefits and obstacles, and contextual enablers and constraints. Thematic analysis of  NVivo-coded transcripts identified three core dimensions shaping effective human–AI cooperation: technological infrastructure readiness, cultural receptivity among practitioners and the rigour of data-privacy safeguards.

Building on these findings, phase two surveyed four hundred educational leaders using measures of infrastructure maturity, stakeholder trust, perceived usefulness, perceived ease of use and data-security confidence. Analyses in SPSS 28 — including exploratory factor analysis, multiple regression and structural path modelling — examined how these dimensions affect decision latency, predictive accuracy and transparency. Results show that AI applications (notably student-assessment analytics, personalized learning recommendations, workflow streamlining and strategic-planning systems) materially improve decision quality when paired with adequate infrastructure and governance. Moderation tests indicate institutions with robust infrastructure and stringent data-governance realise the largest gains, while cultural acceptance mediates the translation of technical capacity into routine practice. K–12 respondents emphasised intuitive interfaces and targeted professional development; university respondents prioritised cross-departmental data interoperability and advanced analytics.

We recommend accelerating the development of interoperable campus-wide and inter-institutional information ecosystems; delivering tiered, role-specific training and change-management initiatives to build trust and uptake; and strengthening educational data-governance and privacy protocols to ensure transparent, sustainable and equitable AI deployment. The chapter offers a theoretically grounded, practically applicable model for balancing AI-driven analytics with human-centred judgement, providing policymakers and educational leaders with a roadmap for responsible, high-impact AI integration in educational administration.