Source: The Conversation – Canada
Artificial intelligence has moved quickly from being an emerging technology to one that is rapidly forcing universities to make consequential choices.
The federal government is investing heavily in university-based research in artificial intelligence, and has introduced an AI literacy strategy that includes a commitment to reaching one million entry-level post-secondary students.
Research on technological change in higher education has repeatedly demonstrated that access to technology doesn’t, on its own, transform institutions. Digital transformation involves shifts in organizational culture, professional capability, governance, infrastructure and leadership alongside technological adoption.
All this raises larger questions: How will universities determine what it means to be ready for AI? What initiatives are universities developing related to AI? And how will this interact with universities’ missions?
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What are universities for? Canadian higher education is at a critical crossroads
15 different universities
Together with Guy Levesque, executive director of the Hunter Hub for Entrepreneurial Thinking at the University of Calgary, I conducted a systematic scan of publicly visible artificial intelligence (AI) activity of 15 research-intensive universities in Canada between March and June 2026.
The research is yet to be peer-reviewed, and the scan is not intended to give a comprehensive picture. It aims to provisionally and comparatively look at how and where AI initiatives are evolving. The goal is to help think about how universities are choosing distinct models of AI integration, and to invite reflection.
The findings from our scan suggest there is no singular trajectory or path for responding to and adopting AI: rather, Canadian universities are making different choices about teaching, learning, governance, entrepreneurship and their relationships with the wider AI ecosystem, and what they want AI to do within their institutional vision and mission.
Publicly documented AI initiatives
We examined the 15 university websites, and also scraped them with search terms and key words. We identified 953 publicly documented AI initiatives from the universities in question related to teaching, student learning and AI literacy, programs and credentials, entrepreneurship, external collaboration, infrastructure, events, funding, student groups and institutional governance.
We then theorized different institutional models related to responding to and innovating with AI, such as: governance and co-ordination; entrepreneurship; teaching and learning-first practices; domain-specific AI models (for example: particular labs in different fields).
Our comparative scan suggests some universities emphasize research and external partnerships related to AI. Others have built strong entrepreneurship pipelines. Some prioritize institutional co-ordination and governance. Others distribute AI innovation across faculties and units.
Still others have concentrated on teaching, professional learning and AI literacy.
A teaching gap deserves attention
One striking finding of our scan concerned teaching. Of the 953 initiatives identified, only 91 — approximately nine per cent — were explicitly instructor-facing teaching initiatives.
That deserves attention because instructors are highly consequential for AI implementation. They are being asked to determine when AI supports learning, when it undermines intended learning, how assessment should change, what responsible use looks like and what students need to know before delegating tasks to AI.
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Why the real test of Canada’s AI strategy is in university classrooms
Emerging work on AI literacy increasingly emphasizes critical evaluation, ethical reasoning and informed decision-making rather than merely knowing how to operate AI systems. UNESCO has similarly emphasized a human-centred approach to generative AI in education that maintains human agency, inclusion and accountability.
Yet the institutional response can easily become tool-centred: provide access to an AI platform, publish guidelines and assume adaptation will follow.
Our findings suggest that universities may need to invest just as deliberately in the human capacity surrounding AI as in AI itself.
AI literacy and AI credentials: not the same
Another important distinction emerged between AI literacy and credentialization. Universities are developing certificates, microcredentials, degree programs and executive education related to AI. Eight of the 15 institutions were monetizing some form of AI education.
At the same time, institutions are creating free student resources, library guides, responsible-use materials and other supports to help people understand and use AI.
Credentialization asks how universities prepare graduates and professionals for an AI-mediated labour market. AI literacy asks a broader question: What should members of a university community understand about AI in order to engage with it critically, responsibly and effectively?
An institution can potentially be strong in one and comparatively underdeveloped in the other.
That distinction will become increasingly important as universities decide whether AI competency should belong primarily within specialized programs or become part of the capabilities expected of graduates across disciplines.
How to govern AI use?
Across the 15 universities, institutional size did not reliably predict more initiatives, AI-related research output or demonstrable gains in AI entrepreneurship (measured in things like disclosed projects, hackathons, incubators, startups and grants). Nor did greater centralization of AI governance.
As universities determine what governing bodies, research and administration offices are charged with guiding AI use and innovation, as well as AI-related research and entrepreneurship, insights about organizational readiness for change are important. Sustainable change depends on whether organizational members share commitment to change and believe they have the collective capacity to implement it.
Universities don’t innovate alone
One of our scan’s limitations is that understanding institutional comparisons based solely on the number of AI initiatives can be misleading. An institution’s AI capacity cannot be understood solely by looking inside the institution.
Canada’s national AI institutes — Amii in Edmonton, Mila in Montréal and the Vector Institute in Toronto — create relationships among universities, researchers, governments and industry. These can extend institutional capacities beyond what appears on a university website.
Relationships with regional ecosystems influence access to expertise, research networks, talent development, industry collaboration and commercialization.
Moving beyond the ‘AI race’
The argument is sometimes made that changes ushered in by AI are inevitable, suggesting the need for simultaneous rapid AI adoption and AI frameworks to guide use.
A sense of pressure around an “AI race” could suggest every university is on the same “track,” adopting AI in similar ways, with some simply further ahead.
Our findings suggest, instead, that as many paths exist towards AI adoption and use, it’s important for university leaders to carefully consider questions:
• Are students developing the AI literacy and competencies they need to make informed judgments?
• Are instructors being supported to redesign learning and assessment?
• Are governance structures clear enough to support experimentation while managing risks?
• Do entrepreneurship and commercialization efforts connect to the institution’s educational and public missions?
• Are external AI partnerships being treated as part of institutional capacity rather than peripheral relationships?
• Do these pieces form a coherent institutional approach?
The answers will, and probably should, look different from one university to another.
Guy Levesque, executive director of the Hunter Hub for Entrepreneurial Thinking at the University of Calgary, co-authored this story.
Nadia Delanoy does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
Original source: https://analysis1.mil-osi.com/2026/10/12/universities-are-making-different-choices-about-ai-how-will-they-fit-with-their-public-missions/