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The framework conceptualizes academic training governance as an integrated
system of four interdependent pillars: data governance, digital platform governance,
data-driven decision-making, and AI ethics governance. These pillars operate as mutually
reinforcing components rather than isolated mechanisms. Data governance establishes
the foundation for managing educational data. Digital platform governance provides the
infrastructure for coordinating academic activities. Data-driven decision-making
transforms data into actionable insights, while AI ethics governance ensures responsible
and accountable use of algorithmic systems. Together, these pillars form a coherent
governance model for managing digital learning ecosystems in AI-enabled higher
education.
4. Discussion
The proposed framework suggests that the transformation of academic training
governance in the digital era is fundamentally institutional rather than purely
technological. While digital infrastructures and AI expand universities’ technical
capabilities, their impact depends on governance mechanisms that structure how data,
platforms, and decision processes are coordinated. In this sense, digital transformation
shifts the locus of change from technology adoption to governance redesign.
4.1. Governance shift: from administrative governance to data governance
A central implication of the framework is the transition from administrative
governance to data-centric governance. Traditional academic training governance relies
on hierarchical structures, periodic reporting, and fragmented information flows. These
arrangements are increasingly inadequate in digital environments where data are
continuously generated and require real-time processing.
Data governance becomes the foundation of this shift. It determines how
educational data are collected, integrated, and used across institutional units. More
importantly, it redefines authority in governance systems. Decision-making is no longer
based primarily on administrative hierarchy but increasingly on data availability and
analytical capacity. This transformation alters the nature of coordination within
universities, moving from procedural control toward information-based governance.
However, data-centric governance also introduces new dependencies. Universities
must ensure data quality, interoperability, and accessibility to avoid fragmented or
unreliable decision processes. Without strong data governance, the expansion of digital
systems may increase complexity rather than improve coordination. Thus, academic
training governance transformation is not simply about using more data, but about
institutionalizing data as a strategic resource.
4.2. Institutional capacity for digital universities
The framework also highlights the importance of institutional capacity in enabling
academic training governance transformation. Digital universities operate through
interconnected platforms that support teaching, administration, and decision-making.
Governance effectiveness depends on the ability to coordinate these systems rather than
manage them in isolation.
Digital platform governance plays a critical role in this process. It ensures
interoperability between systems and enables integrated information flows across the
institution. Without such coordination, digital infrastructures risk becoming siloed,
limiting their contribution to academic training governance. Digital platform governance
therefore acts as a structural mechanism that connects technological systems with
organizational processes.

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