In 2015, a KPMG study reported in ����Vlog noted that universities����a year in “complying with measures to uphold academic standards”. A decade on, and with more compliance obligations than ever, that figure must be much higher.

The pursuit of fairness and consistency is a laudable aim in itself, and its achievement would be worth a lot. However, the elaborate bureaucratic systems of assessment that we have constructed – grading, rubrics, compliance forms – may inadvertently subvert the real purpose of higher education.

The institution built to nurture enquiry has increasingly become one that merely measures, audits and verifies it. We produce abundant evidence of learning, but not necessarily more learning itself. Indeed, the more we measure, the less we seem to trust what cannot be measured. If we don’t see it, it doesn’t exist.

The rise of����has deepened the problem. Intended to ensure transparency, metrics quickly distort behaviour the moment performance is judged by them. When targets become goals, they start to govern attention. Courses are shaped to accommodate measurable outcomes and goals, assignments written to satisfy grids, and students learn to simulate curiosity rather than inhabit it – if they bother at all.

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What begins as a measure of performance, then, ends as its imitation. Grading and assignment design, now so often anticipatory of pragmatic metrics, turn content itself into something cheap and disposable – endlessly generatable, endlessly assessable, but rarely deep.��

Artificial intelligence enters this terrain as an accomplice rather than an intruder. It can produce model essays, mark at scale, and flag deviation with remarkable precision. And if assessment is treated as a technical process to be perfected, rather than a human encounter that reveals understanding, machines fit neatly into the university’s logic. AI offers speed, consistency and the illusion of objectivity – precisely the virtues bureaucracy prizes. What it cannot do is help anyone learn how to think – or spark the curiosity that propels genuine enquiry.��

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Ironically, the rise of AI only heightens the need for academics to push back against this hollowing out of learning. When originality can be simulated so effortlessly, content – facts – becomes cheaper still, and the ability to think critically becomes both harder to test and more crucial to preserve.

The purpose of higher education always was to cultivate�������Ի���: clarity of ideas while remaining open to what we do not yet understand. When employers ask “Why should we hire you?”, the answer was never supposed to be��“because I have high grades and/or a certificate”. It was supposed to be “because I know how to think, to reason, to collaborate, to learn in uncertainty”.

Yet bureaucracy����that complex human judgement can be made reliable by procedure. It translates messy, creative, qualitative processes into neat administrative boxes. By contrast, thinking, especially the kind education is meant to cultivate, resists standardisation. We cannot bureaucratise our way into producing better thinkers, any more than we can automate wisdom.��

Indeed, anyone who has seen a student wrestle with a difficult idea knows that learning involves a leap beyond what any rubric can capture. It draws on interpretation, empathy and ethical judgement. These are capacities that develop only when the pressure to satisfy the metric subsides.

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Bureaucracy, with its����of procedures and protocols, imprisons minds rather than nurturing them – it produces and demands conformity to rules even when they no longer make sense. Deviations from these rules are not appreciated. Yet, genuine critical capacity depends on what escapes this cage: the freedom to make and repair mistakes, to struggle with ideas and contrary schools of thought, and above all, to pursue ideas, not because they fit a grid but because they matter.

Navigating the current administrative set-up in an effort to respond to the opportunities and challenges of AI is like trying to use the same tracks for high-speed trains as were used for steam engines. They may look similar but, as a former railroad engineer, I can assure you that there is a world of difference.

To respond to the challenges the advent of AI poses, we may need to rip up the existing tracks and rethink higher education considerably, always allowing for the possibility that trains of thought in the classroom might veer off in untimetabled directions – including into interesting sidings.

��is an associate professor at the��University of Bath School of Management.

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