Teach.Learn.Share

In the finale of this three-part episode, the conversation steps back to ask a fundamental question: what is a university degree for in an AI-shaped world? The framework of “stuff, skills, and soul” is introduced to help answer this question and explore how universities can prepare students for an uncertain future of work. While content and technical skills remain essential, the discussion emphasizes the growing importance of human capacities such as judgment, resilience, adaptability, and working with ambiguity. Drawing on research and historical examples, the guests argue that AI is more likely to raise expectations around critical thinking than eliminate work outright, while complicating entry-level pathways and transitions from education to employment. The episode concludes with a call for universities to remain nimble, diverse, and committed to cultivating human purpose in an AI-mediated world.
Did you miss the previous episodes? Listen to part 1 and part 2.
Read the transcript. 

In part two of this three-part episode, the conversation turns to the hard questions gen AI raises for teaching and assessment. Moving beyond promise and possibility, the guests examine practical and structural challenges facing universities, including privacy, ethics, access, and uneven adoption. They highlight a deeper pedagogical concern: increasingly “frictionless” AI tools may undermine the productive struggle essential to learning, critical thinking, and skill development. The discussion explores what this means for course design and assessment, calling for structural changes that emphasize process, dialogue, and applied learning—while acknowledging faculty workload pressures and the temptation to revert to traditional exams. The episode concludes by underscoring the importance of grounded, community-based faculty support, shared resources, and practical examples that help instructors adapt without starting from scratch. 
Read the transcript
Did you miss part 1? Listen here. 

In part one of this three-part episode, senior academic leaders from McGill University, the University of British Columbia, and the University of Toronto reflect on generative AI as a transformative opportunity for teaching and learning in higher education. The conversation moves beyond seeing gen AI solely as a disruption or tool as we explore its potential to fundamentally reshape assessment, course design, student support, accessibility, and institutional practices. Gen AI is highlighted as a powerful catalyst—one that invites universities to re-examine long-standing assumptions about teaching, learning, assessment, and the core mission of higher education itself.  
Read the transcript. 

Can open conversations about AI help students reflect on their learning? In this episode, Prof. Nikki Lobczowski shares how she models transparent use of generative AI, while encouraging her students to think critically about how their own use of AI tools might support their learning (or not). The conversation addresses common concerns—including AI literacy, assessment, and time constraints—and emphasizes the value of gradual changes over sweeping course redesigns.   
Read the transcript. 

Can gen AI help students become better scientists? Dr. Jasmin Chahal thinks so—if students learn to question the output first. In this episode, Jasmin shares how she integrated gen AI into a microbiology lab course through a low-stakes, reflective assignment. Hear how her students learned to question AI-generated references, evaluate reliability, and develop critical thinking skills essential for the future of science. 
Read the Transcript.