Case-based learning is a proven pedagogical strategy in medical laboratory science (MLS) education, fostering critical thinking, data interpretation, and clinical reasoning skills. This article describes an innovative "artificial intelligence (AI)-first, student-corrected" assignment model that leverages generative artificial intelligence (AI) as a drafting tool while positioning students as expert validators of clinical information. Students use AI to generate initial case studies based on current course material, then critically appraise and correct inaccuracies in laboratory analytes, reference ranges, diagnostic pathways, and clinical reasoning. A structured rubric guides revision across four domains: patient background, laboratory data integration, evidence-based case questions, and diagnostic conclusions. Importantly, students also create paired videos to demonstrate comprehensive knowledge as well as patient-professional communication skills. Following implementation, MLS students who had completed both the traditional and AI-enhanced versions of the assignment were surveyed. Of the 17 students who responded, 15 (88%) preferred this AI-enhanced approach over traditional case writing, citing improved engagement, reduced writer's block, enhanced error detection skills, and increased confidence with emerging AI tool usage. By transforming students from passive case consumers into active builders of clinical understanding, this model strengthens essential MLS competencies, including critical appraisal, data verification, and AI literacy, all while preparing learners for a future where AI-assisted tools are embedded throughout laboratory medicine. This pedagogical innovation demonstrates how educators can harness AI's efficiency while preserving and amplifying the cognitive and professional benefits of case-based learning.
Nadine Lerret (Fri,) studied this question.