Educated workers are already using AI
Generative AI is quickly becoming part of Canadians’ working lives. An analysis of the Canadian Survey on Working Conditions by Statistics Canada found that generative AI was the most prevalent technology used at work from September 2024 to July 2025. The proportion of workers who used generative AI nearly doubled over the survey period, increasing from 17% in September 2024 to 30% in July 2025.
AI adoption has not been concentrated among workers with the least education. The opposite pattern has emerged. Workers with university credentials were among those most likely to report using these tools.
The same Statistics Canada analysis found that workers with a bachelor’s degree or higher were five times more likely to have used generative AI in the last 12 months (37%) than those with a high school diploma or a lower level of education (7%).
The evidence does not point to a choice between education and AI. It shows educated people already working with AI. Because when change accelerates, the rational response is more learning, not less.
Access is not the same as understanding
The ability to produce an answer should not be confused with education. If graduates leave knowing how to produce answers but not how to examine them, the institution has not done enough.
Generative AI can propose solutions, identify patterns and present information confidently. It can also produce inaccurate information, amplify bias and overlook important context.
To recognize a weak answer, a person needs enough subject knowledge to know what may be missing. To challenge a recommendation, they must be able to question its assumptions, evaluate the evidence and consider the consequences. This is where universities play a critical role.
A strong university education takes students through a coherent body of knowledge. It exposes them to competing perspectives and asks them to complete increasingly complex work. Students must apply ideas, defend conclusions, respond to criticism and explain their reasoning. These are some of the same capabilities required to use AI well.
Accountability cannot be automated
AI can help produce work, but it cannot accept responsibility for that work. It cannot assume professional responsibility for a harmful recommendation, repair trust after a poor decision or explain why efficiency was valued over fairness. The person or organization using the system still owns the outcome.
Students must learn that accountability cannot be delegated. This becomes increasingly important as AI contributes to decisions affecting businesses, employees, customers and communities. The more powerful the tool, the more important it becomes to understand who remains answerable for its use.
