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Figure 1. Conceptual framework of restructuring academic training governance in the
AI-enabled era
Source: Author
3.3. Propositions of the conceptual framework
Based on the four-pillar framework, this study formulates four propositions
explaining how governance mechanisms support the transformation of academic training
systems in AI-enabled environments. These propositions highlight the relationships
between key governance dimensions and the effectiveness of academic training
governance.
Proposition 1 (P1). Data governance as a foundational condition. Effective data
governance provides the institutional foundation for AI-enabled academic training
governance by ensuring data integrity, accessibility, and responsible use. Universities with
strong data governance are better positioned to support data-intensive management and
analytical decision-making.
Proposition 2 (P2). Digital platform governance as an enabling infrastructure. Digital
platform governance enables the integration and coordination of digital infrastructures
that support academic activities. Well-governed platforms facilitate information flows,
institutional coordination, and cohesive digital learning environments.
Proposition 3 (P3). Data-driven decision-making as a governance capability. Data-
driven decision-making enhances the responsiveness and effectiveness of academic
training governance. Universities that apply learning analytics systematically are more
likely to support evidence-based management and strategic planning.
Proposition 4 (P4). AI ethics governance as a regulatory safeguard. AI ethics
governance ensures that Artificial Intelligence applications remain transparent,
accountable, and aligned with academic values. Clear ethical guidelines and oversight
mechanisms help maintain institutional trust and legitimacy.

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