← Back to all news
arXiv AI By Xinyu Li, Zijian Li, Mengyu Xia, Luzhen Tang, Naping Chen, Changmin Lin, Danijela Gasevic, Dragan Gasevic, Yizhou Fan

From GenAI Virtual Patient Dialogue Logs to Teacher-Interpretable Process Evidence: A Learning Analytics Study in Higher Education

Read the original on arXiv AI →

The study investigates whether coded dialogue logs from generative AI-powered virtual patients can provide teacher-interpretable evidence of clinical reasoning. Analyzing 1,030 dialogues from 210 second-year medical learners, the researchers applied behavioural prevalence, Epistemic Network Analysis, and Transition Network Analysis to identify process patterns linked to high-rated history-taking performance. Findings show that high-rated consultations involve more integrated information gathering, communication, and synthesis, rather than merely increased volume of activity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Sep 12

Evaluating Scaffolding-Oriented Multi-Agent Large Language Model System for Clinical Interview Training

The study introduces a scaffolding-oriented multi-agent Large Language Model (LLM) AI Standardized Patient (AI-SP) platform designed to train medical students in patient interviews. In a randomized controlled trial with 100 students, the multi-agent system—comprising a patient agent, a Socratic tutor agent, and a turn-level evaluator—did not improve diagnostic accuracy but significantly enhanced overall OSCE scores, especially in communication, empathy, and history-taking. The authors also release a richly annotated dataset to support further research in AI-supported clinical reasoning training.

By Luming Yang, Haoxian Liu, Siqing Li, Rong Jia, Yue Xiao, Guanhua Chen, Li Lu
llmsagents
More like this →
arXiv AI
Jun 17

AIPatient Arena: EHR-grounded evaluation of large language models in end-to-end clinical consultation workflows

arXiv:2606. 17474v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly considered for use in clinical consultation tasks, yet most medical evaluations remain static, single-turn, or narrowly outcome-based, limiting their ability to reflect the sequential, uncertain, and interactive nature of real-world care.

By Jiahui Niu, Huizi Yu, Wenkong Wang, Guangxin Dai, Jingxian He, Xiang Li, Zhiying Liang, Xinxin Lin, Kent CY So, Bryan YP Yan, Yun Kwok Wing, Yanqiu Xing, Xin Ma, Lizhou Fan
llmssafety
More like this →