My research program bridges two lines of inquiry: (1) the neurobiology of affect and stress-related psychopathology and (2) discipline-based education research (DBER) focused on how emerging AI tools—especially generative AI—reshape learning, assessment, and instructional design in analytics-oriented business education. In my earlier and ongoing biomedical work, I investigate neural and immunological mechanisms underlying anxiety and affective disorders, with emphasis on risk factors that influence onset and persistence (e.g., inflammation, genetic predisposition, circadian disruption, and microbiome development). A major focus has been the role of the serotonin system in the production and maintenance of psychopathology. Using a range of experimental paradigms—including anxiogenic pharmacology, knock-out models, circadian disruption, and neurodevelopmental models—I have quantified expression changes in key serotonin-related genes and examined how immunoregulatory and anti-inflammatory interventions may prevent or attenuate maladaptive affective outcomes. A current priority is characterizing microbiome–host interactions that drive these molecular changes, including how microbiome perturbations influence gene expression and related behavioral phenotypes. My current educational research examines evidence-based integration of AI in teaching and learning. My current work with DBER focuses on how generative AI can support students’ analytical reasoning, feedback-seeking, and skill transfer—while also assessing risks (overreliance, inequities, privacy, and academic integrity). This work includes designing and evaluating AI-enabled instructional interventions (e.g., structured prompt literacy, and course-specific assistants)
keywords
Serotonin, psychopathology, microbiome, anxiety, depression, therapy, AI teaching, AI use
