
Join us as we unpack the HYP-MIX framework, a groundbreaking approach that leverages Large Language Models (LLMs) to create realistic and adaptable simulations of learner behavior in interactive learning environments.
We'll discuss the challenges of designing effective open-ended learning environments and how simulations can help streamline development and testing.
Discover how the HYP-MIX framework utilizes Marginalized Distributional Hypotheses (MDHyps) to predict learner actions and inform the design of personalized learning experiences.