Universities are at a critical point as students and faculty embrace the use of AI. Already, universities like Harvard are introducing courses that feature an AI avatar as an “instructor.”
The promises AI offers higher education are familiar: an increase in efficiency as a consequence of less administration, more teaching coverage, better feedback and smarter assessment. But introducing artificial intelligence (AI) systems in an attempt to fix these problems is not the answer. If anything, AI systems will magnify and speed up the bureaucracy that is driving universities’ decline.
In practice, the more likely outcome is darker: AI will not simply sweep away the bureaucratic machinery of universities; it will give that machinery a new regime of AI compliance — more policies, more forms, more trackers, more oversight roles and more work downloaded onto faculty who are already stretched thin.
The danger is not that AI will destroy universities but that universities will use AI to preserve the very processes that make them dysfunctional. If this happens, we might see faculty spending less time on research, less time on teaching, and more time on activities that do not directly contribute to the production of knowledge and education of students.
This is already evident in the existing assessment system in the U.K., but shades are creeping into systems elsewhere, providing a window into the future for U.S institutions. A single piece of student work may pass through a plagiarism-detection platform, a grading interface, a moderation spreadsheet for faculty to share feedback, a grade-transfer system, and an exam-board record. Each stage generates its own logins, deadlines, exception rules and audit trails. None of this is inherently malicious. Anonymous marking, moderation, appeals, accessibility and academic integrity are legitimate concerns. But when each concern is implemented as a separate system, the combined effect is administrative incoherence.
In essence, faculty become the human interface between incompatible platforms, spending their time reconciling grades and chasing missing evidence rather than teaching. While this example pertains to U.K. universities, the trend to expand the administrative staff extends to North America. AI simply introduces a new opportunity to increase administration.
AI could, in principle, solve this. It could integrate systems, automate routine reporting, draft feedback, identify inconsistencies and remove duplicate data entry. Yet the institutional incentive is rarely to remove work; it is to make work visible. A university that automates a broken workflow does not necessarily simplify it. It may simply produce faster reports about the broken workflow, more sophisticated dashboards and more elaborate evidence of compliance. The bureaucratic impulse survives by changing its vocabulary: risk management, assurance, transparency, governance, responsible innovation.
At least one possible outcome is that universities will create AI-use policies, require module-level AI statements, mandate disclosure forms, introduce approved-tool lists and establish review committees. Faculty will be asked to redesign assessments, monitor suspected misuse, document their decisions, complete training, and explain their approach to students. Much of this labor will be invisible. It will appear not as a new job title but as an additional expectation attached to teaching.
When something goes wrong, the academic is exposed; when the system works, the institution claims the credit. That asymmetry is corrosive.
This creates a familiar pattern: responsibility without authority. Faculty are expected to make consequential judgments about academic integrity, fairness, privacy and assessment validity. But they do not control procurement, system design, institutional policy or disciplinary procedures. When something goes wrong, the academic is exposed; when the system works, the institution claims the credit.
That asymmetry is corrosive. It teaches committed staff that careful judgment is not valued as expertise but rather as a compliance risk to be managed, and the consequences will fall unevenly. Experienced faculty, after voicing their concern to no avail, may leave, retire early or disengage. Early-career academics, already precarious because they don’t have tenure, will have the least power to resist. Students will receive less of the resource that matters most: sustained intellectual attention.
One possible outcome is that universities will encourage the development of faculty AI avatars and cut hours and employment. The institution will then justify hiring more — not fewer — compliance and “educational enhancement” staff to manage and maintain these AI avatars, negating the idea of efficiency and cost savings. In this scenario, bureaucracy will expand to manage the problems created by its own expansion while undercutting quality for the students who are paying high tuition fees. One might need to hire staff for oversight of these avatars, administrators and lawyers to manage licensing agreements, quality control staff in case things go wrong, and IT teams for integrating them into learning platforms and websites.
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There is an alternative to the administrative tasks. AI should be governed by a simple institutional rule: no new compliance requirement without the removal of an equivalent existing burden. If a university requires assessment redesign to accommodate AI, or the introduction of new technology, it should provide protected time, technical support and a single integrated platform. If it expects faculty to manage AI use, it must give them clear institutional backing rather than leaving them to improvise. And critically, it should show how the technology makes work more efficient before rolling it out across the board.
But the deeper issue is not technology but institutional purpose. A university exists — or should exist — to cultivate understanding, judgment and intellectual independence. If its systems consume faculty time, fragment the student experience, and reward documentation over learning, then AI becomes another instrument of managerial self-preservation. The question is not whether universities can adopt AI. It is whether they can resist using AI to automate their own absurdities.
If they cannot, the outcome will be an educational system that looks progressively sophisticated while becoming increasingly hollow — full of monitoring, low on trust; rich in measuring, poor in teaching; obsessed with compliance, indifferent to the people who make education possible; overly procedural sans intellectual life. It will become an audited theater of learning not worth students’ time.
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