What are Agentic AI Systems?
Agentic AI systems are distinguished by three properties: autonomy, goal-directed reasoning, and adaptive behavior. They can decompose complex tasks into smaller actions, execute them across environments, and self-correct based on feedback. For example, while a conventional AI model may generate a report, an agentic AI can autonomously collect new data, analyze patterns, and update that report when circumstances change. In other words, the agent becomes an active collaborator rather than a reactive tool.
This short video introduces the AI4Youth program, how agentic AI works, and how students can build real-world AI agents for social impact.
This autonomy brings both power and responsibility. The agent must operate under human-defined goals and ethical constraints, ensuring its independent decisions remain aligned with societal values. In a community context, this means enabling local stakeholders to define priorities — whether they relate to environmental resilience, education access, or equitable healthcare — and allowing the AI to work toward those objectives adaptively.
Agentic AI for Youth (AI4Youth)
Hands-on, no-code AI and automation for Canadian high school students — turning youth into creators of AI agents, entrepreneurs, and future-ready problem-solvers.
What students leave with
- A functional, deployed “AI agent for good”.
- Job-ready experience in AI automation and prompt engineering.
- Exposure to startup incubators (e.g., Nobellum, Rotman).
- Ongoing mentorship to refine projects and portfolios.
- Offered as virtual Fall/Winter cohorts and in-person summer bootcamps at AIMMLab, reaching 900–1,200 youth over three years.
Program at a Glance
AI4Youth demystifies AI and automation through visual, no-code tools. Students learn how to connect large language models to everyday apps (Teams, OneDrive, email, web forms) and design AI agents that solve real problems in their schools, homes, and communities.
Who It’s For
Grades 10–12 across Canada
- No coding background required.
- Priority for Black, Indigenous, rural, and other equity-deserving youth.
- Open to students curious about AI, automation, or entrepreneurship.
How It Runs
Flexible, blended model
- Virtual Fall & Winter cohorts (32 hours each).
- Two-week in-person summer bootcamps at AIMMLab.
- Hands-on labs, mini-lectures, and team projects.
Why It Matters
From AI literacy to jobs
- Students build a portfolio-ready AI project.
- Learn how AI skills translate into jobs and startups.
- Connect with mentors and innovation ecosystems.
Why AIMMLab?
AI4Youth is delivered by the Artificial Intelligence and Mathematical Modelling Lab (AIMMLab) at the Dalla Lana School of Public Health, University of Toronto. The lab combines expertise in AI, data science, epidemiology, and community engagement across Canada and 20+ countries.
AIMMLab’s team includes Canada Research Chairs, postdoctoral fellows, research staff, and students who regularly work with hospitals, schools, and community organizations to deploy AI tools that matter in practice.
- MathMods: K–12 modelling program reaching 600+ Black students with culturally responsive STEM teaching.
- AI4GHI Challenge & Student Collective: 140–200+ students each year building responsible AI solutions.
- “Meet a Disease Modeller” & “A Day at AIMMLab”: Weekly K–12 visits, shadow days, and school talks.
AI4Youth builds on this foundation to create a focused pipeline for high school students into AI, automation, and entrepreneurship.
Youth in Action: Photo Gallery
The photos below showcase youth from our previous AIMMLab programs building models, presenting ideas, collaborating in teams, and discovering that AI and data are tools they can own and shape.
Expression of Interest
If you are a high school student (Grades 10–12) or an educator interested in bringing AI4Youth to your school or community, please complete our Expression of Interest form.
This helps AIMMLab match you to upcoming cohorts, mentorship opportunities, and summer bootcamps.
Apply to AI4Youth (Expression of Interest Form)Questions? Email aimmlab.dlsph@utoronto.ca.