The costs of an objective structured clinical examination (OSCE) are of concern to health profession educators globally. As OSCEs are usually designed under generalizability theory (G-theory) framework, this article proposes a machine-learning-based approach to optimize the costs, while maintaining the minimum required generalizability coefficient, a reliability-like index in G-theory. The authors adopted G-theory parameters yielded from an OSCE hosted by a medical school, reproduced the generalizability coefficients to prepare for optimizing manipulations, applied simulated annealing algorithm to calculate the number of facet levels minimizing the associated costs, and conducted the analysis in various conditions via computer simulation. With a given generalizability coefficient, the proposed approach, virtually an instrument of decision-making supports, found the optimal solution for the OSCE such that the associated costs were minimized. The computer simulation results showed how the cost reductions varied with different levels of required generalizability coefficients. Machine learning–based approaches can be used in conjunction with psychometric modeling to help planning assessment tasks more scientifically. The proposed approach is easy to adopt into practice and customize in alignment with specific testing designs. While these results are encouraging, the possible pitfalls such as algorithmic convergences’ failure and inadequate cost assumptions should also be avoided.

客观结构化临床考试 (OSCE) 的成本是全球卫生专业教育工作者关注的问题。由于 OSCE 通常是在泛化理论（G 理论）框架下设计的，本文提出了一种基于机器学习的方法来优化成本，同时保持所需的最小泛化系数，这是 G 理论中类似可靠性的指标。作者采用医学院主办的 OSCE 产生的 G 理论参数，再现泛化系数以准备优化操作，应用模拟退火算法计算最小化相关成本的小平面数量，并在各种条件下进行分析通过计算机模拟。对于给定的泛化系数，所提出的方法，实际上是一种决策支持工具，为欧安组织找到了最佳解决方案，从而将相关成本降至最低。计算机模拟结果显示了成本降低如何随着所需的普遍性系数的不同水平而变化。基于机器学习的方法可以与心理测量模型结合使用，以帮助更科学地规划评估任务。所提出的方法很容易在实践中采用，并根据特定的测试设计进行定制。虽然这些结果令人鼓舞，但也应避免可能出现的陷阱，例如算法收敛失败和成本假设不充分。计算机模拟结果显示了成本降低如何随着所需的普遍性系数的不同水平而变化。基于机器学习的方法可以与心理测量模型结合使用，以帮助更科学地规划评估任务。所提出的方法很容易在实践中采用，并根据特定的测试设计进行定制。虽然这些结果令人鼓舞，但也应避免可能出现的陷阱，例如算法收敛失败和成本假设不充分。计算机模拟结果显示了成本降低如何随着所需的普遍性系数的不同水平而变化。基于机器学习的方法可以与心理测量模型结合使用，以帮助更科学地规划评估任务。所提出的方法很容易在实践中采用，并根据特定的测试设计进行定制。虽然这些结果令人鼓舞，但也应避免可能出现的陷阱，例如算法收敛失败和成本假设不充分。