Medical exam question difficulty prediction: An analysis of embedding representations, machine-learning approaches, and input feature impact

These findings offer valuable insights for data-driven measurement practices including Automated Item Calibration, Computerized Adaptive Testing, and Intelligent Tutoring Systems in medical education. Furthermore, this study revealed that the optimal feature set for difficulty prediction is contingent on the item style. Future research should extend this line of inquiry to the difficulty prediction of Multimodal test items. [Box: see text].