The Canadian government’s new artificial intelligence strategy positions AI as a major driver of job creation, economic growth and national competitiveness. It has also drawn some criticism for not providing enough detail on safety and governance.
A central element of the strategy is its focus on AI literacy. This includes a national initiative to provide free AI training for Canadians, ensuring all post-secondary students have access to trusted AI agents and a commitment to reach one million entry-level post-secondary students.
Over the past three years, many higher education institutions have been developing AI literacy educational opportunities to respond to the rapid pace of AI advancement and its implications for teaching, learning, research and operations.
In addition to holding AI literacy workshops, seminars and talks, and being concerned with institutional guidelines and ethical frameworks, universities need to plan for the systematic integration of AI literacy as part of their program learning outcomes.
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From AI literacy to fluency
AI literacy is a set of competencies that enables people to critically evaluate AI technologies, engage in ethical reflection pertaining to AI use, communicate and collaborate effectively with AI and use it as a tool.
What some researchers have called AI fluency should be understood as extending the scope and definition of AI literacy. Where AI literacy gives individuals the foundation to understand and work with AI systems, AI fluency lets them innovate, adapt and create with those tools.
Fluency implies confident, creative and context-sensitive engagement with AI, including the ability to integrate AI into complex work, learning, research and decision-making. This includes judgment and the ability to engage dynamically with Al as part of one’s reasoning as well as being proficient in workflow design and evaluating AI outputs.
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As a professor of information science and a vice dean of postdoctoral studies, I believe universities must shift from basic AI literacy to AI fluency.
Rise of ‘agentic AI’
Some faculties and programs within higher education institutions are concerned with preparing graduates for a future-ready workforce and driving our economy.
Whether or not professional preparedness is a discipline’s focus, higher education also needs to be concerned with equipping graduates with advanced AI skills so they safeguard our democratic systems and enrich our communities.
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Agentic AI systems can now execute multi-step plans, use external tools and interact with digital environments to function as powerful components within larger workflows. This development introduces a new layer of complexity to current post-secondary studies.
Universities must now foster and promote AI fluency and capacity to meaningfully contribute to the development of scholarly and scientific AI agents. For example, Co-Scientist from Google introduces a new multi-agent AI system built with Gemini. This iteratively generates, debates and evolves novel hypotheses for complex scientific problems.
Popular large language models such as Claude, ChatGPT and Gemini and AI research agents such as Elicit, Undermind and Consensus can surface unexpected links between disciplines. This can help researchers identify shared concepts, methods and questions that traditional, discipline-specific approaches often miss.
Adopting AI fluency
By adopting AI fluency as a new educational paradigm, universities can prepare graduates to build credible, evidence-based sociotechnical and multi-agent AI systems.
These could tackle global challenges related to areas such as health, food security, climate science, misinformation, business and social transformation.
AI fluency looks like a deep and critical understanding of the emerging agentic AI concepts and applications to equip students to innovate and create alongside AI systems. This implies data and information literacy as well as digital and algorithmic literacy skills.
AI fluency education should cultivate a critical understanding of digital data and AI sovereignty, alongside the importance of verifying the origin, provenance and credibility of data, sources and citations.
THE CANADIAN PRESS/Jeff McIntosh
AI and workforce readiness
A global survey of 2,700 learners, higher education leaders and employers across six countries conducted by Pearson, an educational and ed-tech company, and Amazon Web Services found that 53 per cent of employers struggle to find AI-ready graduates.
Only 14 per cent of current graduates report a high level of proficiency in applying AI tools to a professional workflow. The survey included responses from the U.S., U.K., Brazil, Saudi Arabia, Vietnam and Malaysia and was supplemented by in-depth interviews with higher education leaders.
An important finding in this study is that building an AI-ready workforce depends on structured systems that connect curriculums to the job market.
Canada’s job market reflected an increasing demand for AI expertise throughout 2025, where three per cent of job listings required AI skills, according to the 2026 Artificial Intelligence Index Report from the Stanford Institute for Human-Centered AI, an interdisciplinary institute to advance AI research.
AI literacy frameworks and AI fluency
AI literacy frameworks have been adopted by a number of universities in North America. Each offer different terminology, abstraction and technical depth. Some examples are:
With the exception of the Digital Education Council model and its three competency levels, the frameworks noted above offer no detailed treatment of AI fluency — particularly its application and integration through the lens of subject expertise or workforce readiness.
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Institutional readiness
Higher education must expand the scope of AI literacy beyond merely supporting existing teaching, research and campus operations. Institutions need to drive workforce readiness, maintain institutional relevance and support economic and democratic vitality.
Preparing students for an agentic AI world and market requires we create opportunities for students to develop subject-specific and domain-focused research ethics and AI competencies. With these competencies they can concern themselves with new analytical frameworks, methodologies and solutions, including enhanced interdisciplinary work and approaches.
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Universities need to be concerned with ensuring research activities, processes and workflows are transparent and reproducible, and that larger AI policy evolves in environmentally conscious ways.
This includes adopting environmentally responsible data centres and compute infrastructure — the technology that powers AI — for AI systems to perform tasks powered by sustainable and renewable resources.
Higher education institutions are uniquely positioned to evolve into AI fluency hubs that drive community and positive societal impact. In this way, they can place themselves at the core of human-centered, ethical and socially responsible AI development.
