Sessions
What’s Coming
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.
Register via ZoomDina Pisareva
Nazarbayev University
AI as a Cognitive Partner in Research and Teaching
What changes when AI is treated not as a shortcut or a tool, but as a genuine cognitive partner — something scholars and students think with rather than merely through? Dina is building a practice around AI-augmented social science and developing some of the first AI-integrated methods courses in the field. In this session, she will share what that partnership looks like across research workflows and the classroom: how it reshapes the questions we ask, what it demands of graduate training, and why she sees the terrain as wide open, with most of the interesting questions still unasked.
Register via ZoomStatistical 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.
Register via Zoom