This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Artificial intelligence has moved quickly from an emerging technology to one that is 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 1 million entry-level postsecondary 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? 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 at 15 research-intensive universities in Canada between March and June 2026.
The research has yet to be peer-reviewed, and the scan is not intended to give a comprehensive picture. It offers a provisional, comparative look at how and where AI initiatives are evolving. The goal is to help people think about how universities are choosing distinct models of AI integration and to invite reflection.
The findings from our scan suggest there is no single 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, as well as what they want AI to do within their institutional vision and mission. We examined the 15 university websites and also scraped them using search terms and keywords.
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 for responding to and innovating with AI, such as governance and coordination; entrepreneurship; teaching and learning–first practices; and 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.
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