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Session Type: Symposium
Assessing learners and professionals with utilization of Artificial Intelligence (AI) and Machine Learning (ML) or through longitudinal frameworks is increasingly implemented across the professions. AI/MI-aided techniques provide innovative ways to collect, analyze, and interpret learner’s learning and assessment data. Changing trends in assessments across health professions prompt opportunities as well as challenges. This symposium is designed to address upcoming trends in assessment shifting focus toward AI/ML and/or longitudinal frameworks and to rethink the balance between formative assessments and learning in the professions.
Using Artificial Intelligence to Aid Feedback and Employee Performance - Kadian McIntosh, University of Arizona; Shripad Sinari, University of Arizona
Artificial Intelligence Applications in Objective Structured Clinical Examinations - Chi Chang, Michigan State University
Blurring Qualitative and Quantitative Approaches with AI Language Models: Applications to Clinical Trust - Brian Christopher Gin, University of Illinois at Chicago; Olle ten Cate, University Medical Center Utrecht; Patricia S. O'Sullivan, University of California - San Francisco; Christy K. Boscardin, University of California - San Francisco
Longitudinal Assessment in Medical Board Certifications: A Focus on Learning and Feedback - Ting Wang, American Board of Family Medicine; Thomas R. O'Neill, American Board of Family Medicine
Measurement Considerations for Formative and Longitudinal Assessments: Revisiting, Rethinking, and Reimaging Our Approaches - Amanda Clauser, National Board of Medical Examiners; Monica M. Cuddy, NBME