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At the same time, data-driven decision-making represents a key governance
capability. The availability of educational data allows universities to move toward
evidence-based management, particularly in curriculum planning and student support.
However, the value of analytics depends on its integration into institutional routines. Data
insights must be embedded in governance processes rather than treated as
supplementary tools.
This perspective suggests that digital transformation requires more than
technological investment. It requires the development of institutional capabilities that
align data systems, platforms, and decision-making practices. Universities that fail to build
such capacity may adopt advanced technologies without achieving meaningful
governance improvements.
The transformation toward AI-enabled academic training governance can be
illustrated through the case of a digitally advanced university in Vietnam, such as the
University of Economics Ho Chi Minh City. In recent years, the university has implemented
integrated digital systems, including learning management platforms, student information
systems, and electronic administrative services.
These systems enable the collection of real-time academic data, supporting data-
driven decision-making in curriculum management and student monitoring. At the same
time, academic training governance challenges emerge in ensuring data integration,
system interoperability, and data privacy. The university has gradually developed
governance mechanisms for coordinating digital platforms and managing academic data,
reflecting key elements of the proposed four-pillar framework.
This case illustrates how digital transformation in higher education requires not only
technological adoption but also the development of governance structures that align data
systems, institutional processes, and accountability mechanisms.
4.3. Ethical governance in AI-enabled educational environments
The increasing use of AI introduces a critical ethical dimension to academic training
governance. AI systems influence decisions related to assessment, learning pathways, and
student performance. While these systems improve efficiency and personalization, they
also raise concerns about transparency, bias, and accountability.
AI ethics governance functions as a regulatory safeguard within the framework. It
ensures that algorithmic decision-making remains aligned with academic values and
institutional responsibilities. Importantly, ethical governance is not external to
governance systems but embedded within them. It shapes how data are used, how
algorithms are designed, and how decisions are interpreted.
Moreover, ethical governance plays a strategic role in maintaining institutional
legitimacy. Universities operate as knowledge institutions with social responsibilities. The
use of AI must therefore be not only effective but also trustworthy. Failure to address
ethical concerns may undermine stakeholder confidence, particularly in data-intensive
environments.
From this perspective, ethical governance should be understood as an enabling
condition rather than a constraint. By integrating ethical oversight with data and digital
platform governance, universities can develop governance systems that support
innovation while ensuring accountability. This integration is essential for sustaining digital
transformation in higher education.
This study contributes to the literature by reframing digital transformation in higher
education as a governance problem rather than a purely technological one. While prior

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