Optimizing anatomy dissection teams using the Yukari method: A peer compatibility-based approach

Abstract

Human anatomy dissection serves as a cornerstone of medical education, fostering not only anatomical knowledge but also teamwork and professionalism. Given the considerable intellectual, physical, and emotional demands of dissection, effective team dynamics are essential for student success. To enhance learning experiences and academic outcomes, we developed the “Yukari method”—an automated system for optimizing anatomy dissection team assignments. This method uses a heuristic local search algorithm to maximize peer compatibility based on student peer preferences and motivation levels collected via a secure web survey. Compared to random and self-selected teams, those assigned using the Yukari method showed approximately a 10% improvement in academic performance. Student satisfaction with Yukari-assigned teams was significantly higher than with random assignment and comparable to self-selection. This increased satisfaction, in turn, correlated with better academic outcomes. These findings suggest that the Yukari method is effective in medical education and potentially useful in other team-based disciplines, such as engineering and social sciences.