CIRPA Artificial Intelligence Forum
The event featured expert speakers and participant discussions that covered fundamentals and best practices of AI/NLP with a focus on practical use cases to enhance the ways you interact with data.
Recordings and presentations can be accessed here with an active paid membership in CIRPA.
Dr. Simon Bates is a multi-award-winning Professor of Teaching in the Department of Physics and Astronomy at UBC Vancouver, who also serves as Vice-Provost and Associate Vice President, Teaching and Learning. His work in the area of technology-led educational enhancement and research, spanning some 20 years, has centered around three basic questions: How well are the courses we teach helping students learn? How might we improve upon these outcomes? How will we know that we have?
Talk summary:
Generative AI tools offer tremendous opportunities to support student learning at scale. They also bring significant challenges and limitations that may make some educators feel anything from healthy skepticism to downright opposition, when thinking about what this means for learning. In this talk, I will present some thoughts on how to navigate these tensions, illustrated by some experimental work on what this might look like in practice.
Dr. Jason Simon serves as Associate Vice President of Data, Analytics and Institutional Research (DAIR) at the University of North Texas. He leads a comprehensive enterprise-wide data warehouse team of 13 that brings together data sources from across campus to promote data informed decision making. With his 30 years of experience in higher education, he is a frequent speaker at local, regional, and national conferences on data, analytics, AI/NLP, and institutional culture.
Talk summary:
With AI/NLP technologies (ChatGPT) now mainstream, leaders and educators from all fields are confronted with the promise and perils of these new tools. Dr. Simon’s talk explores the fundamentals and best practices of AI/NLP and presents practical use cases for IR to enhance the efficacy of institutional research. Regardless of how you view AI, this talk will help you think more critically about these tools and what they can do for students and institutions.