What university leaders must learn in the age of AI

Why does artificial intelligence require a fundamentally different approach to university leadership?

Higher education has repeatedly adapted to technological change – from the printing press and industrialisation to computers, the internet, and online learning. Each transformation reshaped how universities operated, yet none fundamentally altered their intellectual purpose.

But artificial intelligence poses a challenge unlike any university leaders have faced before. Unlike previous technologies that expanded access to knowledge, AI expands participation in intellectual work. That distinction reshapes the nature of university leadership.

Many universities are responding with familiar institutional mechanisms – drafting policies, investing in platforms, revising curricula, and updating academic integrity policies. These responses are necessary, but they risk misidentifying the challenge.

Previous technologies changed how universities stored knowledge, delivered education, and managed operations. Artificial intelligence reaches much deeper. It enters the cognitive domain itself – the very domain universities have long considered their primary responsibility.

For centuries, universities have existed to cultivate human intelligence – to help people learn, reason, create knowledge, and exercise judgment. Today, universities must respond to technologies that increasingly participate in many of those intellectual activities.

That is why AI poses a fundamentally different leadership challenge. The issue is no longer solely about technological transformation; it is about cognitive transformation. Universities are entering a new ecology of intelligence where human and artificial intelligence increasingly collaborate. Leaders who recognise this distinction will be better positioned to shape the next generation of higher education.

AI is not another educational technology

University leaders often compare artificial intelligence to earlier innovations, including personal computers, the internet, smartphones, learning management systems, and massive open online courses. While these comparisons are understandable, they are ultimately misleading.

The printing press broadened access to texts.

The internet expanded access to information.

Online learning expanded access to education.

Artificial intelligence democratises participation in intellectual work.

No prior educational technology has so profoundly altered the cognitive division of intellectual labour. Students, researchers and administrators increasingly rely on AI to generate ideas, analyse information, accelerate discovery and support decision-making across teaching, research and institutional management.

Since ChatGPT’s public release in late 2022, universities have rapidly shifted from debating online learning to reconsidering authorship, assessment, and even the definition of expertise.

The issue is therefore not merely about technological adoption but ultimately about institutional identity. Universities must determine which capacities remain distinctly human in an age when intelligent systems increasingly contribute to intellectual work.

If AI is reshaping how knowledge is produced, universities must reconsider one of higher education’s foundational assumptions: the scarcity of knowledge. Specifically, they must ask which educational resources remain scarce – and therefore valuable.

From knowledge scarcity to judgment scarcity

Universities emerged in societies where access to books, scholarship and expertise was scarce. Their central role was to preserve, transmit and expand knowledge while educating successive generations of scholars. Although the digital age has made information increasingly accessible, universities remain central to helping students interpret complex bodies of knowledge.

Artificial intelligence is now accelerating a far more profound shift. For the first time, students can draw on a rapidly expanding ecosystem of free and commercial AI systems that generate explanations, synthesise information, answer questions, provide feedback, and adapt responses in real time.

Access to intellectual assistance is no longer confined to classrooms, libraries or faculty expertise; it is increasingly available on demand to anyone with an internet connection. As information and participation in intellectual work become more abundant, exercising sound judgment in their use remains a distinctly human responsibility.

The defining scarcity of the 21st century is no longer access to knowledge. It is the capacity to exercise sound judgment amid an abundance of machine-generated information.

This shift compels university leaders to confront a more fundamental question: if knowledge is available almost everywhere, what distinctive value does a university provide?

The answer cannot be to provide more information. What remains scarce is the ability to distinguish evidence from misinformation, signal from noise, insight from superficiality, and wisdom from information alone. Although AI can generate answers at unprecedented speed and scale, evaluating their reliability, contextual appropriateness and ethical implications remains an essential human responsibility.

Universities that continue to define their value primarily by transmitting knowledge may struggle to remain relevant. Their future value will increasingly depend on cultivating discernment, practical wisdom, ethical reasoning and intellectual responsibility.

When information becomes abundant, judgment becomes scarce. The university’s distinctive role is gradually shifting from knowledge transmission toward the cultivation of judgment. Its distinctive contribution will depend less on possessing information than on cultivating uniquely human capacities to evaluate, interpret and responsibly apply intelligence.

As judgment rather than information becomes higher education’s defining contribution, expertise itself must also evolve. The question of what it means to be an expert is one of the central challenges confronting university leaders.

Universities must redefine expertise

One of the most profound questions facing university leaders concerns what expertise itself means.

For generations, universities operated on a simple assumption: students acquired knowledge, faculty possessed expertise, and degrees certified mastery. Artificial intelligence challenges each of these assumptions. Intelligent systems now perform many tasks once considered hallmarks of expertise – from analysing data and writing software to drafting legal arguments and generating technical explanations.

Expertise can no longer be understood simply as the possession of specialised knowledge. Increasingly, expertise lies in framing meaningful problems, orchestrating human and artificial intelligence, critically evaluating machine-generated outputs, recognising the limits of automation, and exercising sound judgment.

The expert of the future will still require deep disciplinary understanding, but what will distinguish expertise will be the ability to integrate human insight with machine intelligence responsibly and purposefully.

This shift has profound implications for higher education. Most curricula, assessment systems and academic credentials were designed for a world where expertise meant acquiring and demonstrating knowledge.

If expertise is now evolving from the possession of knowledge to the orchestration of intelligence, universities must rethink how they cultivate, assess and certify the capabilities that will matter most in the AI era.

Once expertise is redefined, university credentials inevitably come under scrutiny. If degrees were designed to certify knowledge, what should they certify when intelligent systems increasingly share in cognitive work?

Rethinking credentials in the AI era

Artificial intelligence challenges the very purpose of university credentials.

For generations, degrees have signalled knowledge, expertise and professional competence. However, as AI performs increasingly sophisticated cognitive tasks, employers may place less emphasis on what graduates know and more on what they can demonstrably accomplish. Degrees may increasingly certify the capacity to exercise judgment with AI rather than demonstrate knowledge without it.

The implications extend far beyond credentials. As knowledge becomes obsolete more quickly, education can no longer be viewed as something completed in early adulthood.

Universities that thrive will move beyond awarding degrees to supporting lifelong intellectual development.

The challenge facing university leaders is therefore no longer digital transformation alone but institutional redesign. Yet redesigning credentials alone is insufficient. Universities must also reconsider how learning is evaluated, because changing what is valued inevitably changes what must be assessed.

The assessment crisis is really a purpose crisis

Much of higher education’s response to AI has focused on academic integrity, using detection tools, revised policies, and debates about appropriate AI use. These efforts are understandable, but they may address symptoms rather than causes.

If AI can produce a competent essay in seconds, the essay itself is no longer the problem. The fundamental question is no longer how to detect machine-generated work but what universities are trying to assess.

Assessment in the AI era must evaluate judgment, reasoning, creativity and ethical decision-making rather than the recall of information.

The assessment debate ultimately raises a deeper question: What kind of human beings are universities trying to develop? Before universities redesign assessments, they must redefine their educational purpose. The future of assessment is therefore not about AI detection. It is about demonstrating humanity.

Universities must lead human development

The most important lesson university leaders must learn may also be the most counterintuitive: as artificial intelligence becomes more capable, the university’s human mission grows more important.

AI can generate answers, but it cannot determine what is worth asking, pursuing or becoming. Those remain fundamentally human responsibilities.

Throughout history, universities have served purposes far beyond workforce preparation. They have cultivated citizenship, ethical reasoning, leadership, cultural understanding and social responsibility. In an age of intelligent machines, these human capacities become not less important but even more essential.

The defining challenges of the 21st century – from climate change and democratic governance to inequality and the responsible use of artificial intelligence – cannot be solved by computation alone. They require judgment, wisdom and moral leadership.

Rather than competing with AI where machines excel, universities have a historic opportunity to reaffirm their distinctive purpose: cultivating thoughtful human beings capable of sound judgment in an increasingly intelligent world.

From technology leadership to cognitive leadership

If the university’s distinctive contribution is to cultivate practical wisdom and human flourishing, its leadership must evolve accordingly.

For more than two decades, higher education has focused on digital transformation. Artificial intelligence, however, demands a fundamentally different conception of leadership. The question is not how universities should adopt AI, but how they should lead as intelligence is no longer exclusively human.

This requires a new form of intellectual stewardship. Rather than merely managing technology, university leaders must redesign institutions to cultivate judgment, foster responsible human-AI collaboration, and prepare graduates to navigate an increasingly intelligent world.

Ultimately, the AI era is not merely testing universities’ capacity to adopt new technologies. It is a test of whether they can redefine their purpose in a world where intelligence is no longer exclusively human.

The universities that thrive in the AI era will not be those that adopt AI first. They will be those who best understand which forms of human judgment must never be delegated.

James Yoonil Auh is a professor at Kyung Hee Cyber University in South Korea, where he teaches and conducts research on artificial intelligence and global learning systems in higher education. Beyond academia, he has led international education and cultural exchange initiatives across four continents, focusing on sustainability, artificial intelligence, and cross-border collaboration in higher education.

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