Bridging the Gap - Integrating Machine Learning Education into Undergraduate Psychology Statistics
Traditional psychology curricula focus heavily on frequentist parametric statistics (e.g., ANOVA, regression). However, as AI becomes ubiquitous, undergraduates can benefit from understanding the conceptual similarities and differences between these traditional models and Machine Learning approaches. This session aims to explore concrete strategies for introducing AI modeling to psych students. We will discuss how to use existing knowledge of linear models to explain ML concepts. We will also discuss whether AI literacy is best served as an integrated component of existing statistics courses or as a standalone Psychology AI requirement/elective. Participants can share pedagogical tools and reflect on how to transition students from point-and-click software to more flexible modeling frameworks. Whether you are a student, teach statistics, have experience with AI, or mentor students and researchers, join us to brainstorm a modern stats toolkit that prepares the next generation of psychological scientists for an AI-integrated field