Leading Continuous Improvement: Generative Artificial Intelligence and Assessment in Higher Education

Abstract

Assessment is most powerful when understood as a leadership-driven process of continuous improvement—iterative, evidence-informed, and oriented toward genuine learning rather than compliance. However, the coordination costs of meaningful assessment have historically exceeded most programs’ capacity to sustain it. This paper examines how generative artificial intelligence, and in particular retrieval-augmented reasoning environments, can function as leadership-support tools that change the conditions under which assessment becomes practically feasible without displacing the human judgment it requires. Drawing on leadership education and assessment scholarship, we develop a three-level framework—individual and course, program, and institutional—and present six practice-based use cases illustrating AI-supported assessment at each level, including an AI-populated curriculum alignment matrix and an AI-assisted synthesis of external program reviews. We also directly address the tensions practitioners must navigate: data contamination from AI-generated student work, reliability-validity trade-offs, the limitations of AI detection tools, and the faculty professional development gap. Implications for assessment leaders are discussed.