Higher education’s compliance and assessment regimes are colliding with rapid AI uptake, critics say, leaving universities focused on measurable outputs rather than deep learning. A recent analysis argued pervasive auditing, metrics and rigid grading rubrics have made institutions better at documenting learning than producing it—an environment in which AI’s speed and objectivity can entrench superficial assessment practices. At the same time, new agentic AI tools promise to automate student tasks and administrative functions, creating an acute academic‑integrity dilemma. Educators warn that treating assessment as a technical auditing process invites machine grading and AI‑generated submissions without corresponding curricular redesign. Leaders are being urged to rethink assessment design, refocus on human encounters that reveal understanding, and craft academic integrity frameworks that distinguish tool‑enabled learning from outsourcing.