Researchers in academia and industry increasingly propose using LLM predictions of human behavior to pilot, augment, or even replace human data collection. When do these predictions support valid inferences, and when do they mislead? This workshop introduces a practical toolkit for answering that question and for using them in scientifically defensible ways. Drawing on recent frameworks for using and validating LLM predictions as behavioral evidence (Broska et al. 2025; Hullman et al. 2026), participants will learn to assess when predicted responses can be trusted, recognise common failure modes, and combine human and synthetic samples for greater precision without introducing bias. Used carefully, predictions do not replace human samples but help researchers design more informative studies.

Attendees will leave with the concepts and hands-on experience needed to decide whether and how to incorporate LLM predictions into their own research.