When AI outpaces understanding: HE must reclaim authority

Universities have always relied on metaphors to make sense of technological change. The clock disciplined academic time. The factory shaped mass education. The computer reframed knowledge as information. Each metaphor did more than describe innovation; it reorganised how universities understood learning, authority and purpose.

Artificial intelligence now occupies a similar role, but with a critical difference. AI functions not only as a metaphor for intelligence but also as infrastructure embedded in everyday academic life. It increasingly shapes how students write, how learning is assessed, how research is conducted, and how institutional decisions are made.

Unlike earlier technologies, AI does not simply extend human capacity; it unsettles it. That unease is not primarily a technical problem. It reflects unresolved tensions that predate the technology itself: between efficiency and judgement, automation and agency, and scale and meaning.

AI makes these contradictions visible because it operates at the point where educational values are translated into systems. Higher education is no longer merely using AI; in important respects, it is beginning to operate within AI.

The central risk, then, is not that universities will misuse artificial intelligence. It is that they will continue operating while quietly ceding their authority to define what counts as knowledge, judgement and learning.

When technologies evolve faster than institutional understanding, efficiency displaces reflection and convenience masquerades as progress. For institutions whose legitimacy rests on their capacity for critical judgement, this is a risk higher education cannot afford to normalise.

From trade-offs to structural tensions

For universities, this marks a decisive transition.

The challenge is no longer how to deploy AI effectively through optimisation and ethical safeguards, but how to sustain educational authority when judgement, agency and responsibility are increasingly mediated by opaque systems.

What once seemed like manageable trade-offs now appear as structural tensions embedded in everyday academic practice, tensions that cannot be resolved through technical refinement alone. It is within this terrain that the paradoxes of AI in higher education become visible.

Mapping the paradoxes of AI

One way to understand this moment is through what might be called an ‘Atlas of AI Paradoxes’, a framework that identifies the recurring contradictions AI introduces into human institutions, including universities. The point of an ‘atlas’ is not to announce a cure. It is to provide orientation: a way to recognise where the ground is shifting and what pressures are accumulating.

Several paradoxes are already shaping governance decisions, classroom practices, and the legitimacy of academic evaluation:

• Credibility and opacity: AI outputs sound increasingly authoritative even as their internal processes remain inaccessible.

• Scalability and sovereignty: global platforms standardise knowledge while eroding local academic norms and institutional autonomy.

• Delegation and deterioration: offloading cognitive and pedagogical tasks risks weakening judgement and educational depth.

• Emulation and embodiment: AI can mimic expression but cannot assume responsibility for meaning or consequence.

• Prediction and freedom: systems designed to anticipate behaviour subtly narrow the space for exploration and surprise.

• Comprehension and dependence: students and staff rely on systems they cannot meaningfully question or contest.

These are not abstract puzzles. They shape decisions about assessment integrity, data governance, authorship and accountability. When AI enters education, it does not simply add capabilities; it alters what learning is understood to be for and what universities are willing to defend as non-negotiable.

Two paradoxes universities cannot resolve away

Many tensions accompany AI in higher education, but two stand out because they force choices that no amount of procedural refinement can fully avoid.

The first is credibility versus opacity. AI systems now generate analyses, feedback and research outputs that appear increasingly authoritative. Their fluency invites trust. Yet the processes that produce these outputs remain largely inaccessible, even to many people who build and deploy them.

Universities face a stark choice. Either they accept forms of knowledge production they cannot fully explain, delegating epistemic authority to systems that resist scrutiny, or they slow decision-making and research, risking institutional irrelevance in a speed-driven knowledge economy.

In this context, transparency is not merely a technical goal. It becomes a trade-off with competitiveness.

The second is delegation versus deterioration. AI promises to relieve educators of cognitive and pedagogical labour: drafting feedback, organising curricula and shaping assessments. But the more judgement is offloaded, the greater the risk that the capacities universities exist to cultivate, such as interpretation, discernment and intellectual struggle, will erode.

The alternative is no less costly. Insisting on human-centred practices requires resisting systems optimised for efficiency and scale, placing universities at odds with the infrastructures increasingly expected to sustain them.

These are not temporary growing pains. They are structural dilemmas embedded in how AI reorganises authority. Universities cannot optimise their way out of them. They can only decide, often implicitly, which losses they are willing to absorb and which values they are prepared to defend at the expense of the institution.

Why are the paradoxes intensifying

These paradoxes are not merely theoretical; they surface most clearly in everyday institutional decision-making, where uncertainty is managed through procedural compromise rather than conceptual resolution.

Predictive logic increasingly precedes human choice. Recommendation systems do not merely respond to behaviour; they shape it.

In education, this raises uncomfortable questions: when learning pathways are predetermined, what happens to intellectual wandering, struggle, or transformation? An institution that increasingly pre-sorts learners into predicted futures will struggle to defend the university as a place where surprise remains possible.

At the same time, AI has been integrated into institutional infrastructure. Universities now use algorithmic systems to manage risk, monitor performance and demonstrate accountability.

What began as a disruption has become a redefinition. Institutions once charged with shaping knowledge cultures now find themselves shaped by the logics of the systems they procure.

A widening gap also exists between use and understanding. Students, educators and administrators interact with AI daily without a clear grasp of how these systems are trained, the data they rely on, or the assumptions they encode.

This dependence without comprehension is becoming a defining feature of academic life, quietly undermining the university’s credibility even as it claims to cultivate critical inquiry.

Together, these forces make AI-related paradoxes not transitional challenges but the new terrain of higher education.

The university’s responsibility

Universities occupy a distinctive position in this landscape. They remain among the few institutions designed for long-term reflection rather than short-term optimisation. Their legitimacy rests not only on producing skills but also on cultivating judgement, responsibility and civic agency.

Across many systems, AI is being adopted primarily for administrative convenience: automating grading, monitoring engagement and predicting attrition.

When the university is treated as a data platform, students become users and learning becomes a service. What is lost is formation: the slow development of discernment, ethical reasoning and intellectual independence.

This is why debates about AI-assisted writing, assessment design and authorship are not peripheral skirmishes. They are proxy struggles over what the university exists to protect in an automated world.

Faculty deliberations about AI are not distractions from the curriculum; they are rehearsals for democratic reasoning amid uncertainty.

Paradox literacy as a core educational outcome

What higher education now requires is a new form of civic competence: paradox literacy.

By paradox literacy, I mean the capacity to recognise, hold and reason responsibly within enduring contradictions that cannot be resolved through optimisation, rules or technical fixes alone. It is not comfort with ambiguity for its own sake, but the disciplined ability to exercise judgement when competing truths are simultaneously valid.

Paradox literacy recognises, for example, that AI can widen access while deepening inequality, democratise creativity while eroding authorship, and enhance efficiency while hollowing out understanding.

These tensions are not design flaws to be eliminated but structural conditions that shape how intelligent systems interact with human institutions.

UNESCO’s emphasis on ‘human-centred AI’ implicitly calls for this capacity, as does the OECD’s growing focus on agency and responsibility rather than narrow skills. Regulation and technical governance can set boundaries, but they cannot cultivate judgement. Universities must.

Paradox literacy, therefore, belongs across the curriculum. Engineers must learn to design with moral awareness. Educators must balance innovation with presence.

Policy specialists must evaluate systems whose impacts are uneven and context-dependent. Artists and humanists must interrogate how meaning shifts when machines participate in expression.

The goal is not to eliminate complexity but to inhabit it responsibly without surrendering the university’s role as a guardian of judgement.

A global responsibility

These challenges are unevenly distributed. Data from some regions is used to train systems deployed elsewhere. Educational technologies developed in the Global North often spread globally without adequate attention to cultural context, epistemic justice or institutional sovereignty.

Universities have a responsibility to confront these asymmetries. Ethical reflection on AI cannot be confined to Silicon Valley, Brussels or Seoul. It must include perspectives beyond the institutions that currently dominate AI design and governance.

In this sense, AI governance in universities is inseparable from global citizenship education. Both demand dialogue across differences and a shared vocabulary for ethical complexity, and both require institutions to defend the conditions that allow plural knowledge to survive.

Keeping the right questions open

The Atlas of AI Paradoxes offers no easy solutions. What it offers is orientation. It helps institutions name the unease that accompanies rapid technological change and resist the temptation toward premature closure.

Some values should remain complex. Justice cannot be fully optimised. Neither can meaning nor dignity. Artificial intelligence will continue to advance. The decisive question is whether universities will keep pace with ethical understanding.

If higher education can reclaim its role as a steward of complexity rather than a manager of efficiency, it can help societies enter the age of intelligent systems with discernment rather than confusion. True intelligence has never been about solving problems alone. It is also about knowing which questions must remain open.

The age of artificial intelligence is also the age of human judgement. Universities must lead that conversation, or we risk building systems that know everything yet understand nothing.

James Yoonil Auh is the chair of computing and communications engineering at KyungHee Cyber University in Seoul. He has worked across the United States, Asia and Latin America on projects that link ethics, technology and education policy.

This article is a commentary. Commentary articles are the opinion of the author and do not necessarily reflect the views of University World News.