Flexible Survival Analysis Teaching Strategies for Graduate Students in the Age of AI

Thomas M. Menino Convention & Exhibition Center 

Effective teaching of applied survival analysis requires ongoing adaptation to meet changing student needs and leverage evolving tools. Drawing on over a dozen years teaching epidemiology doctoral students, I will discuss how my approach has evolved in software tools, teaching methods, and the integration of theory with practice, while maintaining focus on core principles of rigorous applied analysis. This talk will cover key aspects of this evolution, including the transition from Stata to R/Quarto and strategies for making complex concepts accessible to epidemiologists, such as reframing competing risks methods through the familiar lens of sensitivity analysis. Most recently, generative AI has emerged as both an opportunity and challenge, causing me to rethink what I need to teach and how I need to teach it. I have maintained strong integration of didactic principles with hands-on application, a balance that remains critical as students gain access to AI-generated code.

Pedagogical adaptation

Epidemiology education

R and Quarto

Reproducible research