Problem
Research questions and friction points this paper is trying to address.
Pediatric SIC
Communication Training
Multi-party Interactions
Feedback Dynamics
SIC-Agents: Benchmarking and Building an Adaptive Simulator for Pediatric Serious Illness Communication Training
🤖 AI Summary
为解决儿科重症沟通培训不足问题，研究者与教育者合作开发了SIC-Agents，一种适应性模拟器，并通过PitfallBench和DialogueBench进行评估。
📝 Abstract
Pediatric serious illness communication (SIC) is critically important, yet scalable communication training for clinicians remains limited. Compared with other dialogue simulation settings, pediatric SIC poses additional challenges, including multi-party interactions, response to parental distress and strong dependence on feedback dynamics. Existing LLM-based simulators optimize generic dialogue quality rather than curriculum-contingent behavior required for effective SIC training. In collaboration with educators and pediatric clinicians, we introduce the first benchmark suite and simulation framework tailored to pediatric SIC training. Our benchmarks, PitfallBench and DialogueBench, evaluate simulators both at the turn-level and across full dialogues. We further propose SIC-Agents, a self-improving framework that generates a clinician-editable skill document to guide simulator behavior. Our experiments show that SIC-Agents outperforms static expert prompting. To support future research, we release our benchmarks for parent simulation in pediatric SIC at https://github.com/Beikewzh/sic-benchmarks
Innovation
Methods, ideas, or system contributions that make the work stand out.
Adaptive Simulator
Pediatric Serious Illness Communication
Self-Improving Framework
Benchmark Suite
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