Keith Belinko, PhD, is a Senior Consultant with Doyletech Corporation and has more than 40 years of experience in intellectual property management, technology transfer and commercialization.  

Are university students adequately prepared to enter a workforce that is being rapidly reshaped by artificial intelligence?  Are universities evolving quickly enough to prepare them for this new reality?

If a gap does exist, it needs to be addressed directly and institutionally to avoid undermining graduate employability, academic relevance and public trust in university education.

Artificial intelligence is not just a tool that automates routine tasks. AI is altering the structure of entire professions. Employers in sectors as diverse as health care, architecture, engineering, public policy, accounting  and design increasingly expect graduates to work productively with AI systems, understand their limitations, and bring uniquely human value that AI cannot replicate. 

The challenge is not necessarily that jobs will disappear entirely, but that job roles will be reorganized around AI. Students must therefore be educated not only in disciplinary knowledge but in how that knowledge is applied, augmented, and sometimes redefined through AI.

Universities currently remain constrained by slow curriculum cycles, limited faculty development, and institutional habits that were built for a different era. While many programs introduce AI in small ways, few treat AI as a structural shift requiring systemic educational redesign. 

Many faculty members themselves are still learning how AI impacts their field and are understandably cautious about integrating it into their curriculum. Meanwhile, employers continue to move ahead at an accelerated pace.

The risks associated with AI

Artificial intelligence offers clear benefits to university students, but it also introduces significant risks that institutions cannot ignore. One of the most immediate concerns is academic integrity.

As AI tools become more capable of generating essays, solving problems and writing code, the line between legitimate assistance and misconduct can blur, undermining trust in assessment of the student’s capabilities. This challenge is likely compounded by inconsistent policies and enforcement across institutions, which can create confusion and perceptions of unfairness.

Beyond integrity, there is a deeper educational risk: over-reliance on AI can weaken learning itself. When students routinely depend on analysis, writing or problem-solving on AI systems, they may bypass the cognitive effort required to develop critical thinking, reasoning and communication skills.

The result can be impressive outputs without genuine understanding, leaving students ill-prepared for exams, professional practice or situations where AI tools are unavailable or inappropriate.

A recent study by the Massachusetts Institute of Technology used electroencephalography to show that essay writers given access to AI exhibited significantly lower levels of cognitive activity than participants who did not use AI. Moreover, as the study progressed, individuals in the AI-enabled group increasingly relied on the technology, with many resorting to copying entire blocks of AI-generated text into their essays.

Artificial Intelligence also poses risks related to accuracy and judgment. These systems can produce information that sounds authoritative but is incomplete, biased, or in the worst case, simply wrong. Students who lack the experience to critically evaluate AI outputs may unknowingly incorporate errors into their work, with potential consequences in their field of learning.   

Equity and ethics add another layer of concern. Unequal access to advanced AI tools may advantage some students over others. 

Finally, widespread AI use can strain the relationship between students and faculty. Instructors may grow more suspicious of student work, while students may feel increasingly scrutinized for their effort.

Over time, this erosion of trust risks shifting the focus of higher education away from mentorship and learning toward compliance and enforcement.

Rethinking AI in higher education

Teaching students to use AI responsibly means treating it as a creative partner rather than a shortcut. Faculty should encourage students to experiment with AI-generated concepts while requiring them to justify their choices through grounded arguments. Assignments should reward thoughtful decision-making and critical reflection, and not simply polished renderings enhanced by AI.

For faculty to fulfill these responsibilities, institutional support is necessary. The university should provide professional development opportunities that help instructors become comfortable with AI-enhanced tools. Technical specialists could assist in integrating these tools into teaching.

Clear institutional policies are also needed to guide the ethical and respectful use of AI.  

Given the realities of AI, faculty has a responsibility to position students for long-term success. To prepare students for an AI-transformed workforce, the faculty should adopt a coordinated set of actions that reflect both the opportunities and the disruptions created by AI.

First, all students need a foundational understanding of AI. This does not mean turning every course into a technical seminar, but ensuring that graduates have a clear grasp of what AI can and cannot do, how it is used in professional settings, and the ethical questions it raises. Such literacy should be built into the core curriculum rather than offered as optional workshops or electives.

Strengthening faculty capability is equally important. Many instructors are still developing their own fluency with AI, and it is difficult for them to redesign courses or assessments without institutional support. Structured training, opportunities to collaborate with experts, and incentives to experiment with new teaching approaches will be essential to ensuring that courses remain relevant and credible.

Students also need experience working directly with the AI tools that have become standard in many industries. Incorporating these systems into classroom assignments will help them graduate not only with theoretical awareness but with practical confidence in navigating AI-supported environments.

Finally, students need guidance on how AI is reshaping job roles, how to position themselves for roles that complement rather than compete with automation, and how to build careers that remain resilient in a fast-changing landscape.

Together, these measures will allow the faculty to prepare students not just to use AI, but to thrive in a world being re-shaped by it. This is an opportunity to reaffirm the university’s essential role in shaping the next generation of students to qualify in an evolving workforce.  

On a final note

It is somewhat ironic that Geoffrey Hinton, widely regarded as the “godfather” of artificial intelligence, emerged from academia, having conducted much of his foundational research at the University of Toronto, while universities today continue to struggle with how best to embrace and integrate this very same technology.

Having spent a significant portion of my career in technology transfer and commercialization, I would be remiss not to also acknowledge the broader national implications of artificial intelligence.

Professor Hinton helped lay the intellectual foundations of the modern AI revolution; yet, despite this extraordinary Canadian contribution, Canada has struggled to fully capture the downstream commercial, industrial, and economic benefits of AI.

This gap reflects a familiar and persistent challenge within the Canadian innovation ecosystem; for example, too often, breakthrough ideas developed in Canadian universities, and funded by Canadian taxpayers, are commercialized, and monetized in other countries.

This underscores the need to strengthen Canada’s technology transfer, scale-up, and industrial adoption capabilities, so that scientific leadership can be translated into sustained economic prosperity and long-term strategic advantage for Canada.

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