Speaker
Description
Authors: Eric C. Brown (University of Miami), Filip Bogdan Serban Dragan (University of Miami) Francisco Cardozo (University of Miami)
Background: The ability to provide students with accurate real-time information and real-world scenarios by which they can learn and apply the concepts of prevention science is fundamental for gaining mastery in the field.
Methods: This study presents the development of an automated Generative Artificial Intelligence (Gen-AI) application that will help students learn prevention science concepts and respond to fictitious, yet realistic, case studies that require application of these concepts. Our Gen-AI application allows for a large number of scenarios that can vary multiple variables of the case scenarios such as: health outcomes (e.g., alcohol and drug abuse), settings (community, school, family), etiology (e.g., risk, promotive, and protective factors), and theoretical orientation (e.g., socioecological, life-course, cognitive-based), and intervention type (e.g., family-based programs, policy directives, environmental campaigns).
Results: We developed our Gen-AI application with from a review of 14 different syllabi of graduate-level prevention sciences courses in the U.S., course lectures from the Introduction to Disease Prevention and Health Promotion course taught at the University of Miami, and from exemplars of case studies in prevention science provided by the Association for Prevention Teaching and Research. Preliminary results testing the interaction of the application chatbot with a small (N = 7) sample of students indicated that the chatbot did “very well” and that students “liked very much” using the chatbot to develop a preventive intervention as part of a class assignment.
Discussion: Our presentation will demonstrate the cutting-edge use of Gen-AI-based technology for the dissemination of instruction in prevention science.
| Conflict of interest | no conflicts to declare |
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