The development of a standardized AIHED governance roadmap is essential to define best practices, compliance mechanisms, and long-term monitoring strategies for responsible AI integration in higher education. Research must also focus on designing AI bias auditing tools to detect and mitigate algorithmic bias, ensuring fairness, accountability, and inclusivity in AI-driven decision-making.

Additionally, exploring regional AIHED adaptation strategies is crucial for understanding how policy frameworks, cultural dynamics, and infrastructure disparities shape AI adoption across diverse educational landscapes. Identifying context-sensitive governance models will help institutions develop adaptive AI policies that align with global ethical standards while addressing local challenges.

By adopting the HD-AIHED model proposed in this study, universities and higher education institutions can harness AI's transformative power not just as a 'tool', but as a core 'infrastructure' that upholds ethical integrity and human-centered progress. This Human-Driven AIHED Framework ensures that AI serves as a catalyst for empowerment, rather than exclusion, fostering a future where technology and human expertise collaborate to advance equitable and sustainable education.

Ultimately, the HD-AIHED framework envisions AI as an inclusive, ethical, and transformative force in higher education rather than a disruptive replacement for human intelligence. By fostering collaboration between governments, institutions, industry stakeholders, and students, the framework ensures that AI integration remains human-driven, ethically aligned, and strategically positioned to enhance global education. As AI continues to redefine higher education, its success will depend on our collective ability to balance technological progress with ethical imperatives, ensuring that AI in HE remains a beacon of knowledge, accessibility, and integrity for future generations.
Abbreviations

<table><tr><td>AI</td><td>Artificial Intelligence</td></tr><tr><td>AIHED</td><td>Artificial Intelligence in Higher Education</td></tr><tr><td>AIED</td><td>Artificial Intelligence in Education</td></tr><tr><td>HD-AIHED</td><td>Human-Driven AI in Higher Education</td></tr><tr><td>SWOC</td><td>Strengths, Weaknesses, Opportunities, and Challenges</td></tr><tr><td>HE</td><td>Higher Education</td></tr><tr><td>GDPR</td><td>General Data Protection Regulation</td></tr><tr><td>UNESCO</td><td>United Nations Educational, Scientific and Cultural Organization</td></tr><tr><td>OECD</td><td>Organisation for Economic Co-operation and Development</td></tr><tr><td>TAM</td><td>Technology Acceptance Model</td></tr><tr><td>UTAUT</td><td>Unified Theory of Acceptance and Use of Technology</td></tr><tr><td>DoI</td><td>Diffusion of Innovation</td></tr><tr><td>CLT</td><td>Constructivist Learning Theory</td></tr><tr><td>GPAI</td><td>Global Partnership on AI</td></tr><tr><td>IEEE</td><td>Institute of Electrical and Electronics Engineers</td></tr><tr><td>LMS</td><td>Learning Management System</td></tr><tr><td>EDM</td><td>Educational Data Mining</td></tr><tr><td>MOOCs</td><td>Massive Open Online Courses</td></tr><tr><td>AI Ethics Guidelines</td><td>Ethical frameworks for AI governance in HE</td></tr><tr><td>NLP</td><td>Natural Language Processing</td></tr><tr><td>FATE</td><td>Fairness, Accountability, Transparency, and Ethics</td></tr><tr><td>AI</td><td>Artificial Intelligence</td></tr><tr><td>AIHED</td><td>Artificial Intelligence in Higher Education</td></tr></table>
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