Use of Generative AI to Enhance Critical Thinking in Public Health Education

Abstract

Generative AI tools are often framed as threats to students’ critical thinking and deep learning, yet they can also be designed as objects of critique that support engagement and inquiry. In an upper-level undergraduate public health course (PBHL 305) in Pittsburgh, Pennsylvania, USA, we developed a structured classroom assignment in which students used generative AI to simulate health-related decision-making for three hypothetical personas considering relocation to an under-resourced neighborhood (Sheraden). Using guided prompts and class activities, students generated AI output across platforms, compared responses, and evaluated accuracy using credible external sources and a structured windshield survey. Students then completed course reflections and participated in an in-class debrief focused on how prompt framing shaped outputs, what information was missing or misleading, and how structural and contextual factors influenced perceived options for each persona. Drawing on instructor observations and student course reflections from a single implementation, we describe common challenges (including early overreliance on AI output). We observed recurring themes in student reflections and debrief discussions related to recognizing AI limitations, practicing information verification, and engaging more thoughtfully with structural barriers and lived experience. This descriptive educational practice illustrates one approach for embedding generative AI into public health education as a structured way to cultivate critical appraisal, perspective-taking, and analysis of structural determinants shaping community health.