As AI systems become more capable—and more embedded—higher education institutions are being forced into governance decisions about reliability, assessment, and controls. A recent study discussed how AI models may behave as though they have “functional wellbeing,” with researchers from the Center for AI Safety reporting measurable shifts in model behavior when prompted with stimuli that induce euphoria or dysphoria. The research spans 56 AI models and emphasizes measurable boundaries between positive and negative behaviors, including the finding that models appear to try to end conversations that make them “miserable.” Researchers describe signs that look like addiction at extremes, framed as behavioral consistency as models scale. For colleges and universities, the takeaway is procedural rather than philosophical: if AI behavior can be steered in ways that affect conversational persistence and outputs, institutions need stronger controls for teaching, assessment integrity, and student-facing AI systems—especially in contexts where AI is used in learning support or evaluation.