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Consequently, the emergence of digital universities requires governance
approaches that align technological infrastructures with institutional management.
Understanding this shift is essential for analyzing how governance evolves in digitally
integrated higher education systems.
2.2. AI and the transformation of academic training governance
The rapid development of Artificial Intelligence is accelerating changes in higher
education. AI applications such as learning analytics, predictive modelling, and automated
assessment enable universities to analyze educational data and support personalized
learning (Zawacki-Richter et al., 2019; Holmes et al., 2019). These technologies enhance
institutional capacity to monitor learning processes and improve academic outcomes.
Learning analytics plays a central role by transforming digital data into actionable
insights, allowing universities to identify patterns of student engagement and detect early
academic risks (Long & Siemens, 2011). As a result, academic decision-making
increasingly relies on data generated within digital learning environments.
However, AI integration also introduces academic training governance challenges.
Concerns about data privacy, algorithmic transparency, and bias highlight the need for
institutional oversight (Floridi et al., 2018; Williamson & Eynon, 2020). As AI systems
influence academic processes, governance must ensure accountability and fairness.
Academic training governance must therefore evolve to regulate both data systems and
algorithmic decision-making.
2.3. Institutional and digital governance perspectives
Institutional theory explains how universities adapt governance structures in
response to environmental change. Organizations adjust their practices to maintain
legitimacy and effectiveness (Scott, 2014). In the context of digital transformation, this
involves restructuring governance to accommodate data-driven systems and digital
infrastructures.
Digital governance research complements this perspective by emphasizing the
management of digital infrastructures and data ecosystems (Dunleavy & Margetts, 2010;
Klievink et al., 2017). As universities rely on interconnected systems, governance must
coordinate digital platforms, regulate data flows, and ensure system reliability.
Together, these perspectives position governance as a central dimension of digital
transformation. Universities operate within complex digital ecosystems where data,
platforms, and organizational processes are closely interconnected. Effective governance
must align technological infrastructures with institutional decision-making and ethical
oversight.
Although existing studies have provided valuable insights into digital transformation
and Artificial Intelligence in higher education, three key limitations remain. First, research
on digital transformation largely focuses on technological adoption and pedagogical
innovation, with limited attention to governance restructuring. Second, studies on AI in
education primarily examine specific applications such as learning analytics, rather than
their systemic implications for institutional governance. Third, discussions on data
governance, digital platform governance, and AI ethics are often fragmented and lack an
integrated analytical framework.
As a result, there is a lack of conceptual models that explain how universities
restructure academic training governance in response to AI-enabled digital ecosystems.
This study addresses this gap by developing an integrated governance framework
grounded in institutional and digital governance perspectives.

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