Eight hundred years ago, universities emerged in a world where knowledge was scarce. Students traveled great distances to learn from specific scholars and gain access to ideas that could not easily be found elsewhere. At their core, medieval universities existed to preserve, cultivate, and transmit knowledge across generations.
Today, humanity faces the opposite challenge. Information is no longer scarce; if anything, it is overwhelmingly abundant. Generative artificial intelligence can produce, reorganize, and distribute information at unprecedented speed. If universities were born in an age when access to knowledge was limited, what mission should they serve in an era when answers are available almost instantly?
When Thinking Begins to Be Outsourced
Much of the public conversation about AI in recent years has focused on its capabilities and applications. Will it transform industries? Replace certain professions? Reshape labor markets? Yet beneath these questions lies a more fundamental one: How will AI change the way human beings learn, build knowledge, and make sense of the world?
Learning has traditionally begun with uncertainty. Students read to understand, explore to discover, discuss to test and refine their ideas, and reflect to develop their own worldview. What shapes a person is not only what they know, but how they come to know it. AI is increasingly compressing that journey.
A single prompt can generate a complete answer. A research topic can yield an instant summary of decades of scholarship. A programming task can produce functional code within seconds. The gains in efficiency are undeniable. Yet as searching, organizing, comparing, analyzing, and even aspects of reasoning are increasingly delegated to AI, our relationship with thinking itself begins to shift.
Scholars have described this phenomenon as cognitive outsourcing—the transfer of cognitive work from the human mind to external systems. Whether this shift will fundamentally alter how people learn, reason, and exercise judgment remains an open question. What is already clear, however, is that AI is no longer merely a tool for extending human capabilities. It is beginning to reshape the very processes through which knowledge is formed and decisions are made. This is not simply a technological revolution. It may well become a transformation in how humanity thinks.
What Makes AI Good?
More than two thousand years ago, Aristotle argued that what distinguishes human beings is not merely the possession of knowledge, but the capacity to pursue the good. Today, humanity has created machines capable of generating knowledge. AI can answer questions, synthesize information, draw inferences, and in some domains outperform even the most highly trained experts. Yet a fundamental question remains: Can it know what is good?
Knowledge and judgment are not the same thing. AI can calculate the most efficient route, but it cannot determine where we ought to go. It can analyze vast amounts of data, but it cannot tell us what kind of society we should strive to build. It can generate countless options, but it cannot define what constitutes a meaningful life.
This is why classic ethical dilemmas, such as the famous trolley problem, continue to resist definitive answers. Many of life’s most consequential decisions do not have a single correct solution. From a pure utilitarian perspective, sacrificing one person to save five may seem straightforward. Yet human societies are not built on efficiency alone. Responsibility, trust, empathy, culture, and values all shape our decisions. These dimensions of human experience cannot be fully captured in data, nor can they be optimized by algorithms.
As AI becomes increasingly powerful, the most important human capability may no longer be the acquisition of knowledge alone. It may be the cultivation of judgment. This realization is beginning to reshape conversation across the global technology community. Whether framed as NVIDIA’s vision of Good AI or the broader academic movement toward Human-Centered AI, the central concern is no longer simply whether technology is safe and effective, but whether it advances human flourishing.
When AI is applied to disease prediction, drug discovery, climate modeling, advanced manufacturing, and education, the most important question is no longer What can AI do? The deeper question is What should AI be used for? Ultimately, the future of AI will depend not only on the intelligence of our machines, but on the wisdom of the societies that guide them.
From Responsible AI to Good AI
Universities, governments, and technology companies around the world have increasingly embraced the concept of Responsible AI. Principles such as fairness, transparency, explainability, accountability, and privacy have become central to discussions of AI governance. These principles address an essential challenge: how to reduce risk and ensure that technological development remains aligned with societal norms and values. Yet responsible innovation, important as it is, addresses only part of a larger question. Looking beyond the immediate challenges of governance, we must also ask: What kind of future should AI help humanity create?
This is the central concern of Good AI. If the evolution of AI can be understood as a progression, the first stage focuses on efficiency, the second on augmentating human capabilities, and the third on responsible governance. Beyond these lies a fourth stage: advancing human flourishing.
The first three stages focus on how technology functions. The fourth focuses on why it matters. It asks whether educational opportunities become more equitable, whether technological progress enhances human well-being, whether innovation helps address global challenges, and whether future generations can continue to cultivate creativity, responsibility, and purpose in an increasingly automated world.
These are not merely technical questions. They are educational questions. Universities must be part of this conversation because their mission extends beyond preparing professionals. They cultivate citizens, leaders, innovators, and stewards of societal progress.
At National Yang Ming Chiao Tung University (NYCU), AI is not confined to a single discipline. As artificial intelligence becomes increasingly integrated into cancer diagnosis, precision medicine, neuroscience, smart manufacturing, and semiconductor innovation, the traditional boundaries separating engineering, medicine, the humanities, and social sciences are rapidly dissolving. Preparing students to navigate this new landscape requires more than technical expertise alone. It demands the ability to connect knowledge across disciplines and to consider the broader human consequences of technological change. In many ways, this interdisciplinary vision was one of the central aspirations behind the merger that formed NYCU. Today, it has become more relevant than ever.
The University's Enduring Mission: Cultivating Judgment
AI may assist in diagnosing disease, but it cannot independently resolve questions of medical ethics. Semiconductors may drive global digital transformation, yet they also raise complex issues of geopolitics and supply-chain security. Digital technologies increase efficiency while simultaneously creating new challenges involving privacy, fairness, and governance.
For this reason, I have often described NYCU’s educational philosophy throughthree principles: integration, convergence, and innovation. These are not merely institutional aspirations; they are essential capacities for navigating an increasingly complex world. Students must learn to cross disciplinary boundaries, integrate different ways of knowing, and exercise sound judgment at their intersections. In the age of AI, the ability to synthesize knowledge and create solutions across domains may become one of humanity’s most important strengths.
The grand challenges of our time—from artificial intelligence and population aging to climate change, digital governance, and geopolitical uncertainty—cannot be addressed by any single discipline alone. Universities therefore have an expanded responsibility: to connect diverse forms of knowledge, bridge generations, and foster dialogue across sectors of society.
Meaningful innovation often emerges at the boundaries. The university of the future must be more than a center of expertise. It must be a platform for intellectual exchange, public deliberation, and collective imagination.
What Still Makes Us Human?
Eight hundred years ago, universities answered the question of how to preserve knowledge. In the twentieth century, they helped answer the question of how to create knowledge. In the twenty-first century, they may be called upon to address a more fundamental challenge: When intelligence is no longer uniquely human, what still makes us human?
There is no definitive answer. It is a question that must be explored continuously through education, research, public discourse, and dialogue across generations. Universities remain indispensable because they provide the space where society can engage with such questions together.
Knowledge will continue to accumulate. Technology will continue to advance. Algorithms will continue to evolve. Yet questions of purpose, responsibility, meaning, and human flourishing remain beyond the reach of computation alone. And it is precisely these questions—those that AI cannot answer—that define the enduring purpose of the university in the twenty-first century.
The author is President of National Yang Ming Chiao Tung University. This article was originally published by the University of Illinois Urbana-Champaign and is reprinted with permission.
