WO AI NI: Viewing the World Through AI ~ Leveraging Agentic AI for Impactful Higher Education

The development of artificial intelligence (AI), especially generative and agentic AI, offers higher education institutions opportunities to transform teaching and learning, research, community engagement, academic services, and institutional decisionmaking. However, leveraging these technologies requires a solid understanding of how AI and intelligent agents work, along with a governance framework that ensures implementation aligns with the institution’s strategic goals. This presentation explores three interconnected aspects. First, it introduces the fundamental concepts of AI and its evolution toward agentic AI, covering agent characteristics, reasoning capabilities, tool use, planning, and multi-agent collaboration, all of which enable autonomous task execution while remaining under human supervision. Second, it examines a conceptual case study on applying agentic AI to support Good University Governance (GUG). Through a multi-agent approach, agents can collaborate to integrate institutional data, analyze performance, identify challenges, access knowledge and regulations, and generate recommendations for decision-making, all aligned with the principles of transparency, accountability, responsibility, independence, fairness, and effectiveness. Third, it discusses assessing a higher education institution’s readiness to adopt AI and agentic AI, including strategy and leadership, governance, human resources, data, technological infrastructure, business processes, security, and impact evaluation. The discussion also considers the policy context outlined in the draft Framework for the Use of Generative Artificial Intelligence in Higher Education, a document currently undergoing public consultation (as of October 2026) and serving as a developing guide rather than a binding new regulation. The overall content is designed to help higher education leaders understand the potential, risks, and prerequisites for transforming their institutions into more adaptive, responsible, and data-driven organizations. The design for the agentic AI and the assessment framework presented are conceptual in nature and are not intended as an implementation report or evidence of causal impact.