2026-06-08 –, HA Room 2416
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
As a scholar from a historically marginalized background and member of inclusivity-focused fellowships like NIH AIM-AHEAD, I recognize that computational literacy is a major gatekeeper in high-level research. By teaching undergraduate psychology students from all backgrounds how to engage with AI modeling, we can work toward reducingthe digital divide that can often exclude minoritized students from data-driven psychological science. This session will aim to prioritize making ML concepts accessible, ensuring the psychology community is prepared for a rapidly evolving field.
Please note any pre-requisite knowledge/expertise you will expect from attendees (i.e., is the session most appropriate for someone who already has experience with a topic?).:This session is designed for for anyone with interest in education, mentoring, data science, researchers. While no prior experience in AI/ML is required, attendees may benefit a basic understanding of foundational undergraduate statistics (e.g., linear regression, ANOVA). We will be discussing how to translate these familiar concepts into machine learning frameworks
I was born and raised in the Dominican Republic, where I was exposed to poverty, inequity, and a lack of access to adequate mental health care. This early exposure to health disparities motivated me to pursue a career in mental health. As an adolescent, I moved to New York, where I majored in psychology. After graduating, I moved to Boston, working in various settings, including research labs, hospitals, clinics, and community organizations.
My experiences in the Dominican Republic, New York, and Boston have shaped my understanding of mental health and the challenges individuals and communities face. I am currently at George Mason University as a PhD student under the mentorship of Dr. Natasha Tonge. I am committed to engaging my skills, knowledge, and the George Mason community to improve the lives of others and conduct culturally sensitive clinical psychological science.
