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Session Description

This session examines the integration of AI, machine learning, and data-driven approaches in biomedical engineering education. Presentations will explore AI-enhanced instructional strategies, including AI/ML activities for physiology courses and generative AI’s role in instructional labs.

Moderated by

1. Travis Carrell, Uri Feldman, and Dr. Olga Imas

Papers Presented

1. Conducting an International Med-IoT Project under the Innovation-Based Learning Model [view paper]
   Mr. Victor Tsui (University of North Dakota), Mr. Kordell Mitchell Bernaldez Tan (University of North Dakota), and Dr. Enrique Alvarez Vazquez (University of North Dakota)
2. Creating a Predictive Model of Innovation Self-Efficacy Based on Cognitive Dissonance Levels in Innovation-Based Learning Programs [view paper]
   Mercedes Terry (University of North Dakota), Abigail Tubbs (University of North Dakota), Brandon Fugger (University of North Dakota), Blair Dupre (University of North Dakota), Dr. Enrique Alvarez Vazquez (University of North Dakota), and Ryan Striker P.E. (University of North Dakota)
3. Characterizing student adoption of generative AI in technical communication courses [view paper]
   Prof. Angela Lai (Tufts University) and Prof. Kavon Karrobi (Boston University)
4. Developing an AI/ML activity for a BME physiology course [view paper]
   Dr. Laura Christian (Georgia Institute of Technology), Ophelia Anais Winslett (Georgia Institute of Technology), Alpa Gautam (Georgia Institute of Technology), and Dr. Todd M. Fernandez (Georgia Institute of Technology)
5. Exploring the Efficacy of Generative AI and ChatGPT in BME Instructional Labs: A Case Study on GABA Receptors and Synaptic Potentials [view paper]
   Dr. Viswajith Siruvallur Vasudevan (Cornell University), Dr. Shivaun D Archer (Cornell University), and Prof. Jonathan T. Butcher (Cornell University)