Speaker
Description
Preparing future biophysicists requires approaches that connect foundational biological principles with modern computational tools. Our work introduces a Generative‑AI–enhanced framework for teaching bioinformatics, designed to strengthen students’ computational reasoning, data literacy, and engagement with cardiovascular‑related biological systems. This model emphasizes ethical and effective integration of AI outputs into analysis and modeling, helping learners navigate emerging digital research environments. Building on this framework, we developed a MATLAB‑based SpO₂ modeling exercise that guides students through finite‑difference modeling of oxygen transport, clinical decision‑making, and the interpretation of physiological data. By incorporating AI‑generated clinical scenarios into MATLAB workflows, students explore realistic diagnostic pathways and deepen understanding of physiological mechanisms. Together, these innovations create an accessible instructional pipeline—particularly valuable for students across the African diaspora—linking computational physiology, cardiovascular innovation, and AI‑supported reasoning. This combined approach broadens participation in biophysics education and offers scalable models for strengthening quantitative and computational skills in the biological sciences.
