@inproceedings{holzman-etal-2026-clinical,
title = "Clinical Prompt Engineering: Encoding Clinical Knowledge into {AI} Training Simulations - A Crisis Deployment Case Study",
author = "Holzman, Yuval and
Rafaeli, Eshkol and
Elyoseph, Zohar and
Haber, Yuval and
Yirmiya, Karen and
Linkovski, Omer and
Elyoseph, Tal and
Refoua, Elad",
editor = "Zirikly, Aya and
Bar, Kfir and
MacAvaney, Sean and
Ireland, Molly and
Ophir, Yaakov and
Atzil-Slonim, Dana and
Varadarajan, Vasudha and
Bedrick, Steven and
Desmet, Bart",
booktitle = "Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology ({CLP}sych 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.clpsych-1.3/",
pages = "32--42",
ISBN = "979-8-89176-421-7",
abstract = "When large language models simulate patients or clients, they tend to produce cooperative dialogue, premature emotional insight, and rapid resolution. These defaults undermine clinical training, where the pedagogical value lies in sustained difficulty. We describe Clinical Prompt Engineering (CPE), a methodology developed by a multidisciplinary team of clinician-researchers and prompt engineering experts within the [ProjectName] project. CPE encodes clinical knowledge directly into prompt design: each simulated character is constructed through layered psychological profiles, explicit contingency rules linking interactional events to internal states, and enforced non-linear emotional trajectories that resist the model{'}s pull toward resolution. The methodology has been applied across several clinical training simulations involving over 300 participants in formal studies and iterative pilot phases. Each simulated character is embedded within a multi-agent training environment that provides real-time reflective guidance during the interaction and structured, clinically informed feedback afterward. We illustrate the approach through Talking with Lia, a Hebrew-language simulation in which parents practice responding to a seven-year-old child during repeated missile alerts and forced sheltering. The simulation was deployed within the first week of an acute security crisis in Israel in Winter 2026. Of 132 sessions initiated organically through professional networks, 42 were completed; qualitative feedback emphasized the simulation{'}s difficulty as pedagogically meaningful. Because CPE operates at the level of prompt design, it can be developed by clinician-researcher teams and adapted to new populations, developmental stages, and crisis contexts, potentially extending access to expert-informed training beyond the settings where such expertise is typically available. Where much computational work in clinical psychology has focused on classifying mental health states from text, CPE addresses a complementary task: whether clinicians can respond effectively to those states as they shift in real time. The next step is testing whether the skills practiced in simulation transfer to real interactions."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="holzman-etal-2026-clinical">
<titleInfo>
<title>Clinical Prompt Engineering: Encoding Clinical Knowledge into AI Training Simulations - A Crisis Deployment Case Study</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yuval</namePart>
<namePart type="family">Holzman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Eshkol</namePart>
<namePart type="family">Rafaeli</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zohar</namePart>
<namePart type="family">Elyoseph</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yuval</namePart>
<namePart type="family">Haber</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Karen</namePart>
<namePart type="family">Yirmiya</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Omer</namePart>
<namePart type="family">Linkovski</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tal</namePart>
<namePart type="family">Elyoseph</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elad</namePart>
<namePart type="family">Refoua</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Aya</namePart>
<namePart type="family">Zirikly</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kfir</namePart>
<namePart type="family">Bar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sean</namePart>
<namePart type="family">MacAvaney</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Molly</namePart>
<namePart type="family">Ireland</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yaakov</namePart>
<namePart type="family">Ophir</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dana</namePart>
<namePart type="family">Atzil-Slonim</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vasudha</namePart>
<namePart type="family">Varadarajan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Steven</namePart>
<namePart type="family">Bedrick</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bart</namePart>
<namePart type="family">Desmet</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, USA</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-421-7</identifier>
</relatedItem>
<abstract>When large language models simulate patients or clients, they tend to produce cooperative dialogue, premature emotional insight, and rapid resolution. These defaults undermine clinical training, where the pedagogical value lies in sustained difficulty. We describe Clinical Prompt Engineering (CPE), a methodology developed by a multidisciplinary team of clinician-researchers and prompt engineering experts within the [ProjectName] project. CPE encodes clinical knowledge directly into prompt design: each simulated character is constructed through layered psychological profiles, explicit contingency rules linking interactional events to internal states, and enforced non-linear emotional trajectories that resist the model’s pull toward resolution. The methodology has been applied across several clinical training simulations involving over 300 participants in formal studies and iterative pilot phases. Each simulated character is embedded within a multi-agent training environment that provides real-time reflective guidance during the interaction and structured, clinically informed feedback afterward. We illustrate the approach through Talking with Lia, a Hebrew-language simulation in which parents practice responding to a seven-year-old child during repeated missile alerts and forced sheltering. The simulation was deployed within the first week of an acute security crisis in Israel in Winter 2026. Of 132 sessions initiated organically through professional networks, 42 were completed; qualitative feedback emphasized the simulation’s difficulty as pedagogically meaningful. Because CPE operates at the level of prompt design, it can be developed by clinician-researcher teams and adapted to new populations, developmental stages, and crisis contexts, potentially extending access to expert-informed training beyond the settings where such expertise is typically available. Where much computational work in clinical psychology has focused on classifying mental health states from text, CPE addresses a complementary task: whether clinicians can respond effectively to those states as they shift in real time. The next step is testing whether the skills practiced in simulation transfer to real interactions.</abstract>
<identifier type="citekey">holzman-etal-2026-clinical</identifier>
<location>
<url>https://aclanthology.org/2026.clpsych-1.3/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>32</start>
<end>42</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Clinical Prompt Engineering: Encoding Clinical Knowledge into AI Training Simulations - A Crisis Deployment Case Study
%A Holzman, Yuval
%A Rafaeli, Eshkol
%A Elyoseph, Zohar
%A Haber, Yuval
%A Yirmiya, Karen
%A Linkovski, Omer
%A Elyoseph, Tal
%A Refoua, Elad
%Y Zirikly, Aya
%Y Bar, Kfir
%Y MacAvaney, Sean
%Y Ireland, Molly
%Y Ophir, Yaakov
%Y Atzil-Slonim, Dana
%Y Varadarajan, Vasudha
%Y Bedrick, Steven
%Y Desmet, Bart
%S Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-421-7
%F holzman-etal-2026-clinical
%X When large language models simulate patients or clients, they tend to produce cooperative dialogue, premature emotional insight, and rapid resolution. These defaults undermine clinical training, where the pedagogical value lies in sustained difficulty. We describe Clinical Prompt Engineering (CPE), a methodology developed by a multidisciplinary team of clinician-researchers and prompt engineering experts within the [ProjectName] project. CPE encodes clinical knowledge directly into prompt design: each simulated character is constructed through layered psychological profiles, explicit contingency rules linking interactional events to internal states, and enforced non-linear emotional trajectories that resist the model’s pull toward resolution. The methodology has been applied across several clinical training simulations involving over 300 participants in formal studies and iterative pilot phases. Each simulated character is embedded within a multi-agent training environment that provides real-time reflective guidance during the interaction and structured, clinically informed feedback afterward. We illustrate the approach through Talking with Lia, a Hebrew-language simulation in which parents practice responding to a seven-year-old child during repeated missile alerts and forced sheltering. The simulation was deployed within the first week of an acute security crisis in Israel in Winter 2026. Of 132 sessions initiated organically through professional networks, 42 were completed; qualitative feedback emphasized the simulation’s difficulty as pedagogically meaningful. Because CPE operates at the level of prompt design, it can be developed by clinician-researcher teams and adapted to new populations, developmental stages, and crisis contexts, potentially extending access to expert-informed training beyond the settings where such expertise is typically available. Where much computational work in clinical psychology has focused on classifying mental health states from text, CPE addresses a complementary task: whether clinicians can respond effectively to those states as they shift in real time. The next step is testing whether the skills practiced in simulation transfer to real interactions.
%U https://aclanthology.org/2026.clpsych-1.3/
%P 32-42
Markdown (Informal)
[Clinical Prompt Engineering: Encoding Clinical Knowledge into AI Training Simulations - A Crisis Deployment Case Study](https://aclanthology.org/2026.clpsych-1.3/) (Holzman et al., CLPsych 2026)
ACL
- Yuval Holzman, Eshkol Rafaeli, Zohar Elyoseph, Yuval Haber, Karen Yirmiya, Omer Linkovski, Tal Elyoseph, and Elad Refoua. 2026. Clinical Prompt Engineering: Encoding Clinical Knowledge into AI Training Simulations - A Crisis Deployment Case Study. In Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026), pages 32–42, San Diego, California, USA. Association for Computational Linguistics.