@inproceedings{atapattu-etal-2025-exploring,
title = "Exploring the Role of Mental Health Conversational Agents in Training Medical Students and Professionals: A Systematic Literature Review",
author = "Atapattu, Thushari and
Thilakaratne, Menasha and
Do, Duc Nhan and
Herath, Mahen and
Falkner, Katrina E.",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1069/",
doi = "10.18653/v1/2025.findings-acl.1069",
pages = "20785--20798",
ISBN = "979-8-89176-256-5",
abstract = "The integration of Artificial Intelligence (AI) into mental health education and training (MHET) has become a promising solution to meet the increasing demand for skilled mental health professionals. This systematic review analyses 38 studies on AI-powered conversational agents (CAs) in MHET, selected from a total of 1003 studies published between 2019 and 2024. Following the PRISMA protocol, we reviewed papers from computer science, medicine, and interdisciplinary databases, assessing key aspects such as technological approaches, data characteristics, application areas, and evaluation methodologies. Our findings reveal that AI-based approaches, including Large Language Models (LLMs), dominate the field, with training as the application area being the most prevalent. These technologies show promise in simulating therapeutic interactions but face challenges such as limited public datasets, lack of standardised evaluation frameworks, and difficulty in ensuring authentic emotional responses, along with gaps in ethical considerations and clinical efficacy. This review presents a comprehensive framework for understanding the role of CAs in MHET while providing valuable recommendations to guide future research."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="atapattu-etal-2025-exploring">
<titleInfo>
<title>Exploring the Role of Mental Health Conversational Agents in Training Medical Students and Professionals: A Systematic Literature Review</title>
</titleInfo>
<name type="personal">
<namePart type="given">Thushari</namePart>
<namePart type="family">Atapattu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Menasha</namePart>
<namePart type="family">Thilakaratne</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Duc</namePart>
<namePart type="given">Nhan</namePart>
<namePart type="family">Do</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mahen</namePart>
<namePart type="family">Herath</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Katrina</namePart>
<namePart type="given">E</namePart>
<namePart type="family">Falkner</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2025</title>
</titleInfo>
<name type="personal">
<namePart type="given">Wanxiang</namePart>
<namePart type="family">Che</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Joyce</namePart>
<namePart type="family">Nabende</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ekaterina</namePart>
<namePart type="family">Shutova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mohammad</namePart>
<namePart type="given">Taher</namePart>
<namePart type="family">Pilehvar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vienna, Austria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-256-5</identifier>
</relatedItem>
<abstract>The integration of Artificial Intelligence (AI) into mental health education and training (MHET) has become a promising solution to meet the increasing demand for skilled mental health professionals. This systematic review analyses 38 studies on AI-powered conversational agents (CAs) in MHET, selected from a total of 1003 studies published between 2019 and 2024. Following the PRISMA protocol, we reviewed papers from computer science, medicine, and interdisciplinary databases, assessing key aspects such as technological approaches, data characteristics, application areas, and evaluation methodologies. Our findings reveal that AI-based approaches, including Large Language Models (LLMs), dominate the field, with training as the application area being the most prevalent. These technologies show promise in simulating therapeutic interactions but face challenges such as limited public datasets, lack of standardised evaluation frameworks, and difficulty in ensuring authentic emotional responses, along with gaps in ethical considerations and clinical efficacy. This review presents a comprehensive framework for understanding the role of CAs in MHET while providing valuable recommendations to guide future research.</abstract>
<identifier type="citekey">atapattu-etal-2025-exploring</identifier>
<identifier type="doi">10.18653/v1/2025.findings-acl.1069</identifier>
<location>
<url>https://aclanthology.org/2025.findings-acl.1069/</url>
</location>
<part>
<date>2025-07</date>
<extent unit="page">
<start>20785</start>
<end>20798</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Exploring the Role of Mental Health Conversational Agents in Training Medical Students and Professionals: A Systematic Literature Review
%A Atapattu, Thushari
%A Thilakaratne, Menasha
%A Do, Duc Nhan
%A Herath, Mahen
%A Falkner, Katrina E.
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F atapattu-etal-2025-exploring
%X The integration of Artificial Intelligence (AI) into mental health education and training (MHET) has become a promising solution to meet the increasing demand for skilled mental health professionals. This systematic review analyses 38 studies on AI-powered conversational agents (CAs) in MHET, selected from a total of 1003 studies published between 2019 and 2024. Following the PRISMA protocol, we reviewed papers from computer science, medicine, and interdisciplinary databases, assessing key aspects such as technological approaches, data characteristics, application areas, and evaluation methodologies. Our findings reveal that AI-based approaches, including Large Language Models (LLMs), dominate the field, with training as the application area being the most prevalent. These technologies show promise in simulating therapeutic interactions but face challenges such as limited public datasets, lack of standardised evaluation frameworks, and difficulty in ensuring authentic emotional responses, along with gaps in ethical considerations and clinical efficacy. This review presents a comprehensive framework for understanding the role of CAs in MHET while providing valuable recommendations to guide future research.
%R 10.18653/v1/2025.findings-acl.1069
%U https://aclanthology.org/2025.findings-acl.1069/
%U https://doi.org/10.18653/v1/2025.findings-acl.1069
%P 20785-20798
Markdown (Informal)
[Exploring the Role of Mental Health Conversational Agents in Training Medical Students and Professionals: A Systematic Literature Review](https://aclanthology.org/2025.findings-acl.1069/) (Atapattu et al., Findings 2025)
ACL