When Disruption Meets Abdication: Generative AI and the Quiet Collapse of Academic Governance

Interpretive Frame

This essay examines the rise of generative AI in higher education not as a student ethics crisis or a technological inevitability, but as a moment of institutional exposure. By focusing on governance, role clarity, and responsibility, it reframes faculty distress and exit not as individual failure, but as a predictable outcome of administrative ambiguity.

AI as a Stress Test, Not the Problem

The arrival of generative AI did not create a crisis in higher education. It revealed one.

Much of the public and institutional conversation has framed AI as either a student ethics problem or a tooling problem. Students are accused of dishonesty; faculty are encouraged to adopt better detection systems, redesign assessments, or “adapt” their pedagogy. In this framing, the disruption is technological, and the failure is individual.

But this interpretation misses what the moment actually exposed. Generative AI functioned as a stress test—one that higher education institutions were unprepared to pass. When a system encounters a disruption of this scale, the central question is not whether individuals behave perfectly, but whether governance structures clarify authority, responsibility, and shared norms. In many institutions, that clarification never came.

Instead, ambiguity prevailed. Policies lagged behind practice. Enforcement expectations were implied rather than stated. Faculty were told to “use their judgment” without being given institutional backing, consistent standards, or protection when those judgments were challenged. What appeared as flexibility was, in practice, abdication.

The Hidden Transfer of Responsibility

In the absence of clear governance, responsibility does not disappear—it moves downward.

Faculty became the de facto enforcers of standards they did not define, using tools they did not choose, under policies that remained deliberately vague. They were expected to identify AI misuse, confront students, adjudicate disputes, and absorb the emotional and reputational costs of those confrontations—often without administrative follow-through.

This is not a neutral condition. It is a structural one.

Administrative cultures of ambiguity allow institutions to appear responsive without being accountable. When outcomes are contested, responsibility can be deflected: onto faculty judgment, student behavior, or the pace of technological change itself. The institution remains officially concerned, but functionally absent.

Over time, this produces a familiar pattern. Faculty who care deeply about academic integrity experience moral injury as they are asked to enforce standards without authority. Others quietly lower expectations to avoid conflict. Still others disengage, burn out, or leave—not because they reject adaptation, but because the terms of their role have become incoherent.

Role Confusion and Moral Injury

Higher education relies on a fragile moral ecology. Faculty are not merely content deliverers; they are entrusted with evaluative authority that depends on institutional legitimacy. When that legitimacy erodes, the work becomes ethically destabilizing.

Generative AI intensified this instability by collapsing long-standing assumptions about authorship, assessment, and originality. But the deeper harm came from silence at the governance level. Without clear institutional positions, faculty were left negotiating meaning alone: What counts as misconduct? What evidence is sufficient? What risks are acceptable? Who stands behind these decisions?

Briefly acknowledging my own position here is necessary. I am not observing this from a distance. Like many colleagues, I encountered a widening gap between what I was responsible for enforcing and what the institution was willing to support. That gap—not AI itself—became unsustainable.

This is not an individual story. It is a sociological one. When systems refuse to define the boundaries of responsibility, individuals internalize the strain until something gives.

Exit as Rational Response

Faculty exit is often narrated as personal failure, fragility, or resistance to change. That narrative is convenient—and deeply misleading.

Under conditions of prolonged ambiguity, exit can be a rational response to structural incoherence. When expectations are unbounded, authority is undefined, and accountability flows only downward, remaining becomes a form of quiet complicity. Leaving, in this context, is not avoidance; it is boundary-setting.

This does not mean exit is desirable, or that it should be normalized without concern. It means it should be understood accurately. Institutions that treat faculty departure as an individual problem avoid confronting the governance failures that made departure sensible.

The uncomfortable question this moment raises is not whether faculty can adapt to AI. It is whether institutions are willing to govern in its presence.

If they are not, the cycle will repeat. New tools will emerge. New disruptions will arrive. Responsibility will again be shifted downward. And those who care most about the integrity of the work will once again be asked to carry what the system refuses to hold.

That should disturb us—not because it signals technological change, but because it reveals how easily institutions abandon the very people they depend on to sustain meaning, standards, and trust.

How to cite this article:

Green, R. K. (2026). When disruption meets abdication: Generative AI and the quiet collapse of academic governance. The Emergent Self. https://theemergentself.com/when-disruption-meets-abdication-generative-ai-and-the-quiet-collapse-of-academic-governance/