Editor’s note: This is the 14^th article since May 20, 2026 in an ongoing series by Dr. Andrew Maxwell, the Bergeron Chair in Technology Entrepreneurship in the Lassonde School of Engineering at York University. Every week – and occasionally every other week – we’ll present a new article by Maxwell, in a series whose wide-ranging and incisive themes encompass: Canada and innovation policy; productivity and industry; innovation frameworks; AI and higher education; research and intellectual property; technology adoption; entrepreneurship and commercialization; universities and higher education; entrepreneurship education; and AI and the future of work. 

This is Part 2 of a five-part series looking at the future of universities: Why are universities so difficult to change? What does AI mean for learning? If AI can teach, what is the professor for? And if some of the constraints around which universities were designed are disappearing, what might we design instead? Part 1 was published on August 26, 2026.

In Part 1 of this series, I explored why embedding real-world, multidisciplinary, experiential learning remains structurally difficult, even when institutions genuinely want to do it.

Those were necessary diagnoses.

But diagnosis is not redesign.

If the architecture is misaligned, and the pressures are structural rather than cosmetic, then the question is no longer how to improve courses or integrate new technologies. The question is what the university must become under conditions of accelerating change.

And behind that sits an even harder inquiry:

If education transforms fundamentally, is there still a central role for universities at all?

Education is not disappearing. If anything, it is expanding.

Technological acceleration, climate transition, geopolitical instability, demographic shifts and AI-driven labour market change are increasing the demand for human capability formation. Lifelong learning is no longer rhetorical; it is structural.

What is changing is the form education takes.

Learning is becoming continuous rather than front-loaded. It is increasingly personalized rather than cohort-bound. It is modular rather than bundled. It is globally networked rather than geographically constrained. And it is increasingly AI-augmented rather than instructor-dependent.

For the first time in history, motivated learners can access advanced explanations, simulations, adaptive feedback and global intellectual communities without enrolling in a traditional degree program.

AI systems can synthesize across disciplines, generate drafts, debug code, simulate complex systems, and provide iterative guidance at scale.

This does not eliminate expertise.

But it weakens the institutional monopoly over knowledge access.

Universities were built, in part, on the assumption that knowledge was scarce and required structured mediation. That assumption no longer holds in the same way. When access decouples from institution, the institution must redefine its purpose.

AI is not a tool. It is a structural condition.

AI must be addressed directly, not as an enhancement but as an environmental shift.

It dissolves knowledge scarcity. It alters what competence looks like. And it compresses institutional adaptation timelines.

If AI can execute procedural tasks – writing, coding, analyzing, designing – then human value shifts toward framing, judgment, ethical reasoning, interdisciplinary integration and the capacity to question assumptions. In other words, toward curiosity and disciplined inquiry.

At the same time, AI challenges both pillars of the university simultaneously. It reshapes research by accelerating discovery and lowering barriers to analysis. And it reshapes teaching by enabling adaptive, personalized, scalable learning outside institutional walls.

Printing transformed distribution. The internet transformed access. AI transforms cognition.

Universities cannot respond to that shift with policy alone. They must respond architecturally.

There is a quieter shift occurring alongside AI.

Knowledge production and knowledge sharing are no longer confined to institutional boundaries. Open science networks, preprint cultures, global collaborations and AI-assisted research are reshaping discovery. Digital platforms fragment authority. Expertise competes with virality.

Universities historically stood as primary arbiters of validated knowledge. That legitimacy supported both research and teaching. If that epistemic centrality weakens, the educational function weakens with it.

The question is whether universities can remain trusted stewards of knowledge integrity in a decentralized, AI-mediated world. That role cannot be defended rhetorically. It must be demonstrated through transparency, rigor and engagement beyond disciplinary silos.

Research and teaching are linked not merely administratively, but epistemically. If universities cease to be central in knowledge creation and validation, their claim to structure learning around that knowledge erodes.

Curiosity: The missing structural variable

At the heart of both research and learning lies curiosity.

Research thrives on questioning, reframing, hypothesis generation and the willingness to challenge established models. Yet much undergraduate education is structured around replication. Students are rewarded for applying established methods correctly, navigating predefined assessments and optimizing within bounded problem definitions.

This is not accidental. Standardization enables scale and fairness. But under conditions where AI can execute procedural competence more efficiently than humans, the value of compliance declines.

If universities wish to remain central, they must embed a research posture across the educational experience. Not turning every student into an academic researcher, but ensuring that every student develops the capacity to ask better questions, interrogate assumptions, evaluate evidence and iterate thoughtfully.

Curiosity cannot remain the privilege of graduate seminars. It must become infrastructural.

That requires redesigning assessment, reshaping incentives and structuring curricula around inquiry rather than mere coverage.

The degree has long been the organizing unit of higher education. It bundles time, progression and content into coherent signals.

But if learning becomes lifelong, interdisciplinary and personalized, the degree must evolve from a content package into a developmental architecture. This does not mean abolishing degrees. It means stress-testing their rigidity.

Interdisciplinary trajectories, embedded research experiences, reflective portfolios documenting growth, modular stacking of capabilities, and global collaborative learning models should not be peripheral experiments. They should be institutional design inquiries.

The degree should signal not only what a student knows, but how they think, question, integrate, and adapt.

If universities are serious about adaptation, they must apply their research mindset to themselves.

Institutional redesign should not be reactive crisis management. It should be disciplined experimentation. Pilot alternative incentive structures that reward educational innovation. Test new governance models that enable interdisciplinary collaboration. Integrate AI-supported reflective infrastructures and evaluate their impact. Measure adaptability as a performance metric alongside research productivity.

Universities experiment boldly in laboratories. They rarely experiment on their own architecture.

That may be the transformation that matters most.

The risk that accumulates quietly

Institutions rarely collapse dramatically. They drift.

The greater risk facing universities is not sudden disappearance, but marginalization. Education will continue. Learning will expand. Capability formation will reorganize.

If universities do not redesign their architecture to align with how education is evolving, alternatives will grow into the space. Platform-based credential systems, corporate academies, and AI-native learning networks may not replace universities overnight, but they may gradually render them less central.

The risk of experimentation feels immediate and visible. The risk of inertia accumulates slowly –  until it does not.

After considering architecture, AI, curiosity, knowledge authority and experimentation, we arrive at the unavoidable question.

If education becomes continuous, personalized, AI-augmented and globally distributed, do we still need universities?

If the answer is yes, it cannot rest on tradition. It must rest on function.

Universities must articulate and demonstrate the unique public good they provide: stewarding knowledge integrity, cultivating disciplined curiosity, forming professional and civic identity, convening interdisciplinary communities, and protecting intellectual freedom under conditions of rapid change.

If they redesign themselves to amplify those functions, they remain indispensable.

If they do not, education will reorganize without them.

The burden of proof now lies with the institution.

In Part 3, I'll look at: if AI can teach, what exac tly is the professor for?

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