@inproceedings{wang-etal-2026-sic,
title = "{SIC}-Agents: Benchmarking and Building Adaptive Simulator for Pediatric Serious Illness Communication Training",
author = "Wang, Zihan and
Slominska, Anita and
Bimman, Rennie and
Di Flumeri, Elizabeth and
Mayappo-Neeposh, Amanda and
Francoeur, Conall and
Carver, Tamara Ellen and
Chang, Xiao-Wen and
Precup, Doina and
Haritaoglu, Esin Darici and
Haritaoglu, Ismail and
Arodi, Akshatha and
Goloff, Naomi",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1581/",
pages = "35983--36001",
ISBN = "979-8-89176-459-0",
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 and a training interface for parent simulation in pediatric SIC."
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<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 and a training interface for parent simulation in pediatric SIC.</abstract>
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%0 Conference Proceedings
%T SIC-Agents: Benchmarking and Building Adaptive Simulator for Pediatric Serious Illness Communication Training
%A Wang, Zihan
%A Slominska, Anita
%A Bimman, Rennie
%A Di Flumeri, Elizabeth
%A Mayappo-Neeposh, Amanda
%A Francoeur, Conall
%A Carver, Tamara Ellen
%A Chang, Xiao-Wen
%A Precup, Doina
%A Haritaoglu, Esin Darici
%A Haritaoglu, Ismail
%A Arodi, Akshatha
%A Goloff, Naomi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wang-etal-2026-sic
%X 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 and a training interface for parent simulation in pediatric SIC.
%U https://aclanthology.org/2026.emnlp-main.1581/
%P 35983-36001
Markdown (Informal)
[SIC-Agents: Benchmarking and Building Adaptive Simulator for Pediatric Serious Illness Communication Training](https://aclanthology.org/2026.emnlp-main.1581/) (Wang et al., EMNLP 2026)
ACL
- Zihan Wang, Anita Slominska, Rennie Bimman, Elizabeth Di Flumeri, Amanda Mayappo-Neeposh, Conall Francoeur, Tamara Ellen Carver, Xiao-Wen Chang, Doina Precup, Esin Darici Haritaoglu, Ismail Haritaoglu, Akshatha Arodi, and Naomi Goloff. 2026. SIC-Agents: Benchmarking and Building Adaptive Simulator for Pediatric Serious Illness Communication Training. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 35983–36001, Budapest, Hungary. Association for Computational Linguistics.