This presentation demonstrates how artificial intelligence can enhance interpretation and visualization of public health data, emphasizing conceptual understanding over computation. Designed for students analyzing epidemiologic and surveillance datasets, the session shows how AI tools can clarify complex tables and trends through narrative explanation and visual storytelling. Participants will create a coherent dashboard depicting publicly available data student demographics using bar, histogram, and pie charts, plus 1–3 additional visualizations, each with clear titles, labels, and brief narrative text. By integrating AI‑generated insights responsibly, students strengthen core skills in data communication and translational science—learning to explain what data means, not simply calculate it, while respecting the boundary that AI should not replace statistical analysis
As AI tools become embedded in course design, faculty face a growing challenge: AI systems, access, and institutional policies change faster than academic calendars. This interactive session introduces AI-resilient course design, an approach that separates learning intent from tool dependency so learning outcomes remain valid even when AI tools disappear, degrade, or are restricted mid-semester. Participants will apply a practical framework to redesign AI-integrated assignments for durability, rigor, and assessment integrity—using design patterns that can be implemented without changing institutional policies or platforms.
