ChatGPT in the Classroom: Expectations from Students and Instructors

The release of ChatGPT to the public at the end of 2022 caused quite the stir in the educational community. Students and professors reacted with emotions ranging from curiosity, to skepticism, to apprehension. One thing was sure: the impact of Large Language Models (LLMs) in the classroom promised to be significant. Will ChatGPT stifle critical thinking, or can it be used to foster it? How can we attribute and evaluate students’ work in the AI era? And what will students expect to learn from their instructors about proper uses of LLMs? In this panel, students and educators come together to share their experiences, opinions and expectations about LLMs, and how they believe they should be used in higher education.

Terri Griffith holds the Keith Beedie Chair in Innovation and Entrepreneurship at Simon Fraser University’s Beedie School of Business. Her most recent research takes on a “bottom-up” approach to automation, including artificial intelligence. More broadly, she studies how people come to understand and use technology in their work. Her undergraduate degree is from UC Berkeley, and her MS and PhD are from Carnegie Mellon.

Jade Chen is pursuing a major in Psychology with a minor in Data Science at the University of British Columbia. She is passionate about learning the complexities of human behaviour using data analysis, and aims to improve mental well-being with ethical machine learning models.

Kieran Forde is a PhD Candidate in the Department of Curriculum and Pedagogy at the University of British Columbia. His PhD research explores connections between the Right to Be Forgotten and education, especially as it pertains to the increasing commodification of children as data subjects.

Lillian Milroy is a fifth-year Global Health student with a Data Science minor. She is interested in how epidemiological models that fail to account for socioeconomic differences produce results that perpetuate discriminatory assumptions. She hopes to combine her knowledge of global health and data science to design models that better represent populations, more clearly elucidate inequalities and are applicable across varied geographic and cultural landscapes.

Giulia Toti is an Assistant Professor of Teaching in the Department of Computer Science, and 2022-24 Green College Leading Scholar. She holds a PhD in Computer Science, with a thesis on data mining and epidemiology, and a MS in biomedical engineering. Her interests include alternative grading systems for higher education and ethics in data science.

This Leading Scholars series brings together leading experts from diverse fields to explore the transformative potential of Artificial Intelligence (AI) in the context of education at UBC. Through a multidisciplinary lens, this series aims to delve into the promises and challenges that AI presents, beginning with an introduction to generative models for text and images. Then, through a series of panels, organizers will probe into the incorporation of AI in future workflow, on how recent generative models have changed the concept of lifelong learning, the impact of AI on our interactions with images across disciplines, and AI’s potential for language learning, revitalization and reclamation from an Indigenous perspective.

Series Conveners: Anwar Ahmed, Language and Literacy Education; Tamara Etmannski, Civil Engineering; Christopher Hammerly, Linguistics; Giulia Toti, Computer Science; Lily Wenya Zhou, Neurology; Ignacio Barbeito, Forest Resources Management; Katherine Wagner, Economics; Shoufu Yin, History; Thomas Pasquier, Computer Science