Medical Education

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10 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

What Makes Medical Examination Items Difficult? Using Large Language Models to Explore Patterns Associated With Empirical Item Difficulty
Oct 29, 2026Xiaomeng Xiong, Marcus Walker, Yanlin JiangItem Difficulty Estimation Medical Education
University of Florida, Gainesville, Florida, USA. · National Commission on Certification of Physician Assistants, USA.
LLMs as Judges and Examinees for Medical Item Difficulty Prediction
Oct 28, 2026Xiaomeng Xiong, Marcus Walker, Yanlin JiangItem Difficulty Estimation Medical Education
University of Florida · National Commission on Certification of Physician Assistants
REMEDY: How Far Is Video Generation from Medical Education World Models?
Sep 26, 2026Lixing Tan, Yanghao Zhou, Qing Xia +3Medical Education Video Generation Evaluation
Beihang University · Beijing Institute of Technology · Beijing Information Science and Technology University
Evaluating Scaffolding-Oriented Multi-Agent Large Language Model System for Clinical Interview Training
Sep 10, 2026Luming Yang, Haoxian Liu, Siqing Li +4Medical Education Educational Technology
Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA · Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China · Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China +3
MedGame: Storytelling Gamification Empowered by Large Language Models for Medical Education
Jul 23, 2026Qian Wu, Xinrong Zhou, Zizhan Ma +8Medical Education Educational Technology
CUHK · Tencent · Southern Medical University +1
MIRAGE: Retrieval and Generation of Multimodal Images and Texts for Medical Education
May 6, 2026Miguel Diaz Benito, Cecilia Diana Albelda, Alvaro Garcia Martin +3Multimodal IR Medical Education
Universidad Autónoma de Madrid, 28049 Madrid, Spain
What Don't You Understand? Using Large Language Models to Identify and Characterize Student Misconceptions About Challenging Topics
Apr 30, 2026Michael J. Parker, Maria G. Zavala-CernaMedical Education Educational Technology
associate dean for online learning research, faculty director of HMX, Office of External Education, and assistant professor of medicine, Harvard Medical School and Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts · Decanato Medicina, Universidad Autónoma de Guadalajara
PRIME-CVD: A Parametrically Rendered Informatics Medical Environment for Education in Cardiovascular Risk Modelling
Mar 11, 2026Nicholas I-Hsien Kuo, Marzia Hoque Tania, Blanca Gallego +1Medical Education Synthetic Data Generation
Centre for Big Data Research in Health (CBDRH), The University of New South Wales, Sydney, Australia
Artificial Intelligence for Detecting Fetal Orofacial Clefts and Advancing Medical Education
Mar 6, 2026Yuanji Zhang, Yuhao Huang, Haoran Dou +28Medical Education Clinical Decision Support
National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China. · Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen 518060, China. · Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen 518060, China. +14
Dynamic Personality Adaptation in Large Language Models via State Machines
Feb 25, 2026Leon Pielage, Ole Hätscher, Mitja Back +2Medical Education Personality Modeling in Language Models
Institute for Geoinformatics, University of Münster, 48149 Münster, Germany · Faculty of Mathematics and Computer Science, University of Münster, 48149 Münster, Germany · Department of Psychology, University of Münster, 48149 Münster, Germany +1

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What Makes Medical Examination Items Difficult? Using Large Language Models to Explore Patterns Associated With Empirical Item Difficulty

Xiaomeng Xiong, Marcus Walker, Yanlin Jiang

University of Florida, Gainesville, Florida, USA. · National Commission on Certification of Physician Assistants, USA.

Item Difficulty Estimation Medical Education Rubric-Based Evaluation LLM Evaluation

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Research on medical item difficulty has largely focused on prediction rather than on identifying interpretable patterns associated with empirical difficulty. This study used empirical difficulty labels to guide large language models (LLMs) in inducing difficulty rubrics and examined how rubric guidance affected LLM difficulty judgments. Using training items and their empirical difficulty estimates from the United States Medical Licensing Examination (USMLE) and the Physician Assistant National Certifying Examination (PANCE), Claude Opus 5 induced separate rubrics for absolute and pairwise difficulty judgment. None of the induced rubrics treated surface features, such as stem length, option count, or images, as major sources of difficulty. Several patterns emerged across both examinations, including cue convergence, misleading cues, and option competition, although their specific forms differed. Retrieval of knowledge that could not be derived from the stem was particularly prominent in the PANCE rubrics, whereas the USMLE rubrics placed greater emphasis on integrating multiple clinical cues. We compared commercial and open-weight LLMs on held-out items under unguided and rubric-guided conditions. Rubric guidance changed how models used the difficulty scale but did not consistently improve performance. In absolute judgments, guidance encouraged broader use of the scale and generally improved coarse-grained agreement, without consistently improving rank ordering. In pairwise comparisons, its effects varied across models and examinations, and judgments became more sensitive to presentation order. These findings suggest that the value of label-guided rubric induction may lie more in generating interpretable, testable hypotheses from empirical difficulty data than in reliably improving difficulty judgments.