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research has focused on pedagogical innovation and AI applications, this framework
conceptualizes academic training governance as an integrated system of data governance,
platform coordination, analytics, and ethical oversight. By linking institutional theory with
digital governance, the study highlights governance capacity as a key driver of digital
university transformation. This perspective provides a foundation for future research
examining how universities institutionalize AI-enabled governance in practice.
5. Implications for policy and practice
5.1. Macro-level policy implications
At the policy level, governments should establish national frameworks for digital
governance in higher education. These frameworks should include standards for data
governance, interoperability of digital platforms, and ethical guidelines for AI applications.
Regulatory policies should also promote data sharing while ensuring privacy protection
and institutional accountability.
5.2. Institutional-level governance mechanisms
At the university level, institutions need to restructure academic training
governance toward integrated digital academic training governance systems. This
includes: establishing centralized data governance units; developing interoperable digital
platforms; embedding analytics into decision-making processes; creating institutional AI
ethics committees.
Such mechanisms enable universities to align digital infrastructures with academic
training governance processes.
5.3. Implementation roadmap based on the four pillars
The transformation toward AI-enabled governance can be implemented through a
phased roadmap:
Phase 1: Infrastructure integration → develop digital platforms.
Phase 2: Data governance → standardize and integrate data.
Phase 3: Analytics adoption → implement data-driven decision-making.
Phase 4: Ethical governance → institutionalize AI ethics frameworks.
This roadmap highlights that governance transformation is a gradual process
requiring alignment between technological capacity and institutional readiness.
6. Conclusion
This paper develops a conceptual framework for understanding the transformation
of academic training governance in the era of AI-enabled higher education. Drawing on
institutional theory and digital governance perspectives, the study proposes a four-pillar
governance architecture consisting of data governance, digital platform governance, data-
driven decision-making, and AI ethics governance. Together, these pillars form an
integrated governance system that supports the transition from traditional administrative
management toward data-centric academic training governance.
The framework contributes to the emerging literature on digital higher education by
shifting analytical attention from technological adoption to institutional governance
transformation. It highlights that the effective integration of digital technologies and
Artificial Intelligence within universities requires coordinated governance mechanisms
that align technological infrastructures, organizational processes, and ethical oversight.
Future research may extend this conceptual model by empirically examining how
universities implement these governance pillars in different institutional contexts and by
exploring how AI-driven governance mechanisms influence educational outcomes and
institutional decision-making.

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