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SUMMARY:Integrating Generative AI\, Computational Modeling\, and Physiolog
 ical Reasoning to Enhance Biological Sciences Education
DTSTART;VALUE=DATE-TIME:20260327T105000Z
DTEND;VALUE=DATE-TIME:20260327T111000Z
DTSTAMP;VALUE=DATE-TIME:20260426T093152Z
UID:indico-contribution-10338@events.saip.org.za
DESCRIPTION:Speakers: Camellia Okpodu (University of Wyoming)\nPreparing f
 uture biophysicists requires approaches that connect foundational biologic
 al principles with modern computational tools. Our work introduces a Gener
 ative‑AI–enhanced framework for teaching bioinformatics\, designed to 
 strengthen students’ computational reasoning\, data literacy\, and engag
 ement with cardiovascular‑related biological systems. This model emphasi
 zes ethical and effective integration of AI outputs into analysis and mode
 ling\, 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 oxy
 gen transport\, clinical decision‑making\, and the interpretation of phy
 siological data. By incorporating AI‑generated clinical scenarios into M
 ATLAB workflows\, students explore realistic diagnostic pathways and deepe
 n understanding of physiological mechanisms. Together\, these innovations 
 create an accessible instructional pipeline—particularly valuable for st
 udents across the African diaspora—linking computational physiology\, ca
 rdiovascular innovation\, and AI‑supported reasoning. This combined appr
 oach broadens participation in biophysics education and offers scalable mo
 dels for strengthening quantitative and computational skills in the biolog
 ical sciences.\n\nhttps://events.saip.org.za/event/272/contributions/10338
 /
LOCATION:
URL:https://events.saip.org.za/event/272/contributions/10338/
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