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Session Type: Symposium
The rapid development and application of AI in medical education, and broadly professions education, brings challenges to the learners, trainees, and educators in those fields. AI provides promises to escalate the process and release burden of tasks for human users while it may also lead to inaccurate, biased, or unethical practices in teaching, learning, and assessment across professions education. In this symposium, we propose to critically review the current status of ethics considerations and guidelines for AI use that exist in both health and engineering professions education, and we will discuss ethics concerns around use of AI as well as provide practice guidelines and ethical recommendations from an interdisciplinary, mixed-method approach. Future steps will be discussed.
AI, Automation Bias, and Social Accountability in Medical Education - Jane Lindsay Miller, University of Iowa
Human vs. Artificial Intelligence (AI) Inferencing: The Comparison of AI and Simulated Participant (SP) Based Inferencing and Ethical Implications - Amelia Wallace, Old Dominion University
Guidelines for Generative AI in Engineering Design Teaching and Learning - Justin L. Hess, Purdue University
Hallucination vs Interpretation: Rethinking Accuracy and Precision in AI-Assisted Data Extraction for Knowledge Synthesis - Xi Long, University of Illinois at Chicago; Christy K. Boscardin, University of California - San Francisco; Lauren Maggio, University of Illinois at Chicago; Joseph Costello, University of Illinois at Chicago; Yoon Soo Park, University of Illinois at Chicago; Ralph Gonzales, University of California - San Francisco; Rasmyah Hammoudeh, University of California - San Francisco; Ki Lai, University of California - San Francisco; Brian Christopher Gin, University of Illinois at Chicago