Argument in Brief
Higher education quality assurance (HEQA) has traditionally been organized around compliance, periodic evaluation, indicator-based accountability, and external review. While these mechanisms remain necessary, they are increasingly insufficient for supporting continuous quality enhancement and quality culture in complex higher education systems. Current discussions of artificial intelligence in HEQA often frame AI as a tool for automating documentation, accelerating review procedures, or simplifying administrative work. This paper argues that the emergence of agentic AI requires a more fundamental reconsideration of the scope of HEQA. Rather than asking whether AI can make existing quality assurance processes more efficient, the paper asks how AI agents may expand what HEQA can observe, connect, monitor, advise, and support.
The paper develops a Scope Transformation Matrix for analyzing agentic AI use cases in HEQA. The matrix distinguishes between AI applications that merely automate compliance work and those that contribute to quality intelligence, risk-informed governance, continuous monitoring, and human-centred advisory support. It further clarifies the boundary between AI-delegable tasks and human-only judgments, emphasizing that values, accountability, peer review, and final quality decisions must remain human-led. By organizing practical use cases across actors such as teachers, departments, institutions, and QA agencies, the paper offers a conceptual tool for HEQA researchers to examine how agentic AI may support quality culture without reducing quality to datafication or algorithmic control.