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Sessions

What’s Coming


Thursday, September 3, 2026· 7:00 PM ET

Paul Allison

Statistical Horizons

Vibe coding then and now

Allison compares two adventures in developing statistical software for Stata and R: enlisting a human programmer in 2015 and collaborating with AI coding assistants in 2026. The experiences were surprisingly similar—both required clear instructions, careful checking, and occasional expressions of disbelief—but the differences were just as revealing. He considers what AI does better, what it does worse, and why he now generally prefers the machine, despite a few important caveats and the persistent need to know when it is confidently wrong.

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Thursday, October 15, 2026· 7:00 PM ET

Bree Bang-Jensen

Department of International Affairs, University of Georgia

Using AI to extend what students can do

Much of the discussion about AI in teaching focuses on AI literacy or on designing AI-resilient assignments. Bree takes a different angle: how to use AI to extend what students can do rather than supplant it. She will walk through a live demo along with a range of other approaches she has been building into her classroom.

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Date TBD

Nicholas A. R. Fraser

Toronto Metropolitan University

Ethical, Effective, and Transparent Workflows: How to Set Clear Guidelines Surrounding AI Usage for Academic Research

AI poses existential questions for professionals in all fields to a large extent because clear rules and guidelines surrounding ethical AI have not been created yet. How do we set such rules and guidelines? Concerns and controversies about AI usage are rooted in ambiguities about the relationship between human principals and AI agents, and the key is to encourage transparent usage that clarifies this principal-agent relationship. Like data-manipulation computational tools such as R or Stata, AI agents are research tools that require training to use ethically and effectively by human researchers who direct the research process. By proactively establishing this principal-agent relationship through best practices devised through transparent experimentation, academic institutions can effectively mitigate the risks as well as seize opportunities offered by AI tools.

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