Submitted by Curtis Maloley
Resource:
Applying LLMs to a Global Affairs Task
By Danielle Goldfarb, Senior Fellow, Munk School of Global Affairs and Public Policy and Centre for International Governance Innovation at the University of Toronto, and Lecturer in the Master of Global Affairs program.
At the 2025 Technology and Education Seminar and Showcase (TESS), Danielle Goldfarb offered a keynote address entitled, “The Next Generation of Public Interest Intelligence: Using New Tools to Confront the AI, Data, and Trade Shocks.” During her talk, she shared an example of how she integrates generative AI into her teaching by asking students to “apply large language models to specific global affairs challenges and observe both strengths and vulnerabilities.”
Goldfarb’s assessment approach aligns with the University of Calgary’s STRIVE model for designing assessments that effectively incorporate generative AI (itself a worthy find!), and offers a step-by-step process for having students critically engage with Generative AI in addressing a Global Affairs challenge. As she explains, the assignment is effective because:
It emphasizes student-centredness: students experiment with different generative AI tools applied to global affairs challenges they choose. It also emphasizes responsibility: students identify qualities that matter in completing a particular global affairs task and determine where it is and is not appropriate to apply the use of generative AI tools. The assignment also encourages integrity: students test these models for vulnerabilities and strengths, forcing them to consider a range of angles they may not have considered.
This resource includes her full assignment details and student feedback, along with other examples from the University of Toronto.
Curtis Maloley is the Director of Teaching Development and Digital Learning in the Centre for Excellence in Learning and Teaching at Toronto Metropolitan University.
