Rohan Alexander (University of Toronto)
Abstract
One of the joys of teaching data science and statistics is helping students discover how data can spark questions, reveal patterns, support meaningful decisions, and tell a compelling story. Yet the rapid rise of generative AI has changed how students engage with coding, analysis, and writing. Instead of asking whether AI replaces certain skills, we now face more interesting questions: How can these tools open space for deeper learning, better judgment, and genuine curiosity? How can we create opportunities for students to find joy while developing their critical thinking and technical skills in this current moment?
This keynote explores how a “Telling Stories with Data” lens can help us rethink introductory statistics and data science teaching in ways that welcome both AI-enthusiasts and AI-skeptics. We’ll look at emerging research on instructor and student attitudes toward AI, practical strategies for helping students judge data quality and ethics, and ways to help them test, verify, and refine AI-generated work. By shifting emphasis from coding first to questioning first—and from writing to editing—we can give students more room to explore, critique, and experience the wonder that drew many of us to data in the first place.
Bio: Rohan Alexander is an assistant professor at the University of Toronto, jointly appointed in the Faculty of Information and the Department of Statistical Sciences. He is interested in developing workflows that improve the trustworthiness of data science and tends to be especially focused on the role of code and testing. He co-organizes the Toronto Data Workshop, a weekly, free, online, seminar series that brings together academia and industry to share data science and AI best practice. His book, Telling Stories with Data, argues that a trustworthiness revolution is needed in data science, and proposes a view of what it could look like. He holds a PhD in Economic History from the Australian National University.