Abstract:

Addressing the challenges of remediation in medical education, the American University of Antigua College of Medicine developed an innovative approach to support second-year medical students who were repeating coursework. The introduction of the Cognitive Processing Course, which initially involved intensive weekly assignments on histopathological images, marked the beginning of the journey toward enhancing student learning and well-being. Despite its success, the program's demanding nature prompted a shift towards a more sustainable model—Team-Based Learning (TBL).

This revised strategy aimed to preserve the program's strengths while alleviating the workload for both students and faculty. By focusing on historically challenging learning objectives and incorporating artificial intelligence, particularly ChatGPT, we streamlined the content identification process, ensuring a rigorous review to uphold content quality and accuracy. Transitioning to TBL, involving fewer but more focused sessions, resulted in an impressive pass rate of 86% and a marked decrease in faculty workload.

This presentation will explore the integration of strategic technological advancements in academic settings, demonstrating how they can elevate educational productivity, enhance learning outcomes, and reduce the strain on educators. We will share insights and outcomes from our revised remedial program, illustrating how a balanced and technology-informed approach can foster student success and faculty well-being in the demanding context of medical education.

Keywords:

Medical education, Remediation, Team-Based Learning (TBL), Artificial intelligence (AI), ChatGPT, Learning outcomes, Educational productivity, Faculty workload, Student success, Well-being, Academic innovation, Educational technology, Curriculum development.