U of T outlines its approach to AI infrastructure
The university’s AI-ready future could reshape how students learn and navigate university.

Listen to the audio version of this article here!*

Artificial intelligence (AI) is becoming integrated into university practices, especially by 2030, whereby it will play a big role in how students study, brainstorm and work through university as a whole. 

In July 2026, the University of Toronto (U of T) announced a multi-year partnership with Cohere, a Toronto-based AI company founded by former U of T students. According to U of T News, the Cohere North platform is expected to support the university’s upcoming enterprise-wide platform that connects information across university systems while keeping data all within its control. 

For students, this change could impact multiple parts of the university experience, including classrooms, policies, and the Office of the Registrar. 

In 2024, U of T established an AI Task Force to develop a strategy for responding to the rise of generative AI (GenAI). According to U of T’s AI Task Force report, the university’s approach centres on educational elements including active learning, critical thinking development, experiential learning, and responding to technological changes. 

The report highlights students’ access to personalized and interactive support systems for learning, as well as changes in the way instructors redesign assignments. 

In an interview with The Medium, Dr. Christopher Eaton, who studies AI literacy and emerging technologies in education, said, “AI-ready universities should be adaptable.”

Part of that adaptability, he explained, means giving instructors and students room to experiment as the technology changes. It also means recognizing that policies can change rapidly and that AI may make sense for some tasks and have little place in others. Dr. Eaton argues that universities will increasingly have to account for this as part of the learning environment itself, saying that it should be “woven into the fabric of the university structure.”

That could eventually mean moving away from the same chatbot being used for every course. Dr. Eaton envisions smaller and more specialized models, tailored to the culture and students’ needs, as an important benchmark for 2030. 

These specialized models might look like interactions with chatbots that are designed around the materials and learning objective of a course. The skills that students may need to develop may therefore shift from knowing how it should be used to deciding whether it should be used. 

Dr. Eaton highlights that the “ability to evaluate AI outputs will be essential to its evolution,” alongside students’ judgement on how it fits into their learning processes. 

According to the Task Force, AI could help university staff process transfer credits and manage scheduling and enrolment. Undergraduate students who may need additional academic support could also benefit from these systems. Students could also interact directly with university provided AI systems to explore academic opportunities and career paths, find answers to routine questions, or connect with the appropriate staff member when making inquiries.

Accessibility is another point that falls within U of T’s AI vision, as it imagines a wider use of AI for tasks like note taking, lecture transcription, synthesizing text to speech, screen reading and image recognition. 

The report argues that providing AI systems institutionally could make access more equitable, while allowing U of T to evaluate tools for privacy bias and environmental impacts before they reach students. 

Research could also experience changes, as it may involve researchers using AI for tasks such as literature analysis and data management. The report emphasizes that researchers would still need to establish appropriate boundaries for AI use and assess the quality of its output.

U of T currently facilitates its technological adaptations through AI kitchen, which the university describes as a secure environment where vetted AI tools and data can be tested for teaching, research, and administrative projects.

Dr. Eaton stressed that students should have a meaningful role in determining how AI enters their education. “It is easy for learners to become left out of the conversations,” he said. This exclusion could undermine the eventual uptake and success of the technology.

One way to prevent this is to include students as partners in some of the curriculum design. Instructors would continue to determine how a course meets its learning outcomes, but students could contribute to conversations about where AI belongs within that structure. 

Dr. Eaton also pointed to flexibility at the individual level, highlighting that when appropriate, students might have the opportunity “to opt out but with specific caveats” for AI use when it is incorporated into course work.

The Task Force recommends a university-wide AI advisory body that would consult faculty, librarians, staff and students when developing institutional guidance and evaluating new applications. 

U of T’s decision to partner with Cohere adds another dimension because it asks who controls the technology and the data behind it. Cohere AI describes its own technology around the idea of sovereign AI systems that give organizations greater control over their data and infrastructure. Through partnerships, Cohere’s technology is intended to operate within U of T’s own AI environment.

Dr. Eaton sees the Canadian connections as significant for higher education. He pointed to potential advantages for data security, individual property and the university’s ability to build AI systems around its own context. He also stops short of treating sovereign AI as a guaranteed solution. 

“I am skeptical that this is a bit of an idealised vision for AI and sovereign built AI,” he said. Rather, Dr. Eaton described it as a potential “North Star” for how the university approaches AI. 

Becoming AI ready means that the university has to determine how those systems operate in classrooms and how they might operate in student services. For students, the university envisioned that AI may eventually become a part of the infrastructure of university life. This also means that students need to learn when to assess AI and question what it produces.

*AI-Generated Audio Transcription Disclaimer

The audio transcription included in this article was generated using artificial intelligence (AI) technology with the purpose of improving accessibility for our readers and providing an alternative opportunity to engage with The Medium‘s content. Please note that AI-generated transcriptions may contain errors or inaccuracies in pronunciation, wording, or interpretation.

The original written article remains the authoritative version of the content. If you notice an error in the audio transcription, please contact social@themedium.ca so we can for further reviews and corrections.