As artificial intelligence transforms industries, Toronto is emerging as a key player in preparing the next generation for a digital world. But a crucial question is now taking center stage: Who gets left behind?
That question was front and centre at a high-profile panel during the 94th Congress of the Humanities and Social Sciences Canada's largest academic gathering held at George Brown College (GBC) from May 30 to June 6, 2025.
The session brought together leaders in data science, innovation, and education to explore how Canada can build AI literacy without deepening existing inequalities. The discussion featured Debra Lam, Founding Executive Director of the Partnership for Inclusive Innovation, Heather Krause, data scientist and founder of We All Count, and Ryan Morrison, moderator and GBC Professor, English as a Second Language ESL.
Morrison's current research concerns AI and its impacts on the language arts. He said the pace of change with this technology has been relentless.
As he put it, "Every time our [research] group met, something new had happened."
"So, I said, 'Whatever, I'll write a white paper.' And within two years, it was already irrelevant," he added.
Closing the Innovation-Education Gap
The panel emphasized that while AI's potential is enormous, the rollout of AI education must be equitable, deliberate, and community led.
Slowing down and piloting thoughtfully is vital, urged Debra Lam. Such work must be done in collaboration between academia, government, and industry.
"In academia, the big incentive is publication ... [while the] private sector, they need to provide some sort of return [and for the] public sector, they need to report to their constituents," she said. "Once we understand what drives them and what their timelines are, then we can meet in the middle and share the risk if we can do it together."
Toronto, with its world-class education institutions, thriving tech ecosystem, and diverse communities, is uniquely positioned to lead this inclusive approach, panelists argued. Yet only if equity is taken into consideration from the outset, not treated as an afterthought.
Lessons from Georgia: A Cautionary Tale
Lam pointed to her home state of Georgia as an example of what can go wrong when policy outpaces preparation. In 2019, Georgia mandated computer science education for all high school students. But with little investment in training or infrastructure, biology teachers were suddenly tasked with teaching coding.
The result: uneven quality and growing disparities between well-resourced and under-resourced schools.
Speed alone isn't the goal, the panelists agreed. They argued the priority lies in quality, access, and long-term success in adopted AI in an educational setting.
A Blueprint for Purpose-Driven AI Education
Despite the challenges, panelists were optimistic. If educators, policymakers, and tech leaders can work together -- grounded in community needs and cultural humility -- Toronto could become a global model for inclusive, future-ready AI education.
Not just quickly, but inclusively, and with purpose.
George Brown College is helping to shape this future through its Applied A.I. Solutions Development postgraduate program (T431), designed to equip students with real-world AI skills while emphasizing ethics and inclusion.
