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FlexiDialogue: Integrating dialogue trees for mental health with large language models

datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg04:Educação de Qualidade
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorFernandes, João
dc.contributor.authorAntunes, Ana
dc.contributor.authorCampos, Joana
dc.contributor.authorDias, João
dc.contributor.authorSantos, Pedro
dc.date.accessioned2026-06-03T14:02:17Z
dc.date.available2026-06-03T14:02:17Z
dc.date.issued2025
dc.description.abstractThe increasing prevalence of mental health issues among university students is exacerbated by limited access to support due to shortages of mental health professionals and the stigma associated with seeking help. Virtual mental health assistants can extend the reach of existing resources, but traditional systems reliant on scripted dialogues are constrained by inflexibility and limited adaptability to diverse user inputs. This paper introduces FlexiDialogue, a system that transforms rigid dialogue trees into instruction sets for large language models, facilitating dynamic, contextually appropriate, and multilingual interactions while maintaining the structure and quality of expert-validated dialogue flows. The system was evaluated in three phases: (1) determining how effectively large language models could map open-ended user responses to predefined dialogue tree options, allowing for more natural interaction without compromising control; (2) assessing the models’ ability to paraphrase scripted dialogues to improve conversational fluidity while remaining grounded in the original tree; and (3) conducting an expert review to assess overall performance. Results demonstrated that FlexiDialogue enhanced the flexibility and coherence of interactions, with expert evaluations confirming its potential for mental health support.eng
dc.description.sponsorshipSLICE PTDC/CCICOM/30787/2017, IDP/04326/2020, and Project CRAI C628696807-00454142 (IAPMEI/PRR)
dc.identifier.doi10.5220/0013286900003938
dc.identifier.issn2184-4984
dc.identifier.urihttp://hdl.handle.net/10400.1/29086
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSCITEPRESS - Science and Technology Publications
dc.relationInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
dc.relation.ispartofProceedings of the 11th International Conference on Information and Communication Technologies for Ageing Well and e-Health
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMental health virtual assistants
dc.subjectDialogue systems
dc.subjectLarge language models
dc.subjectNatural language understanding
dc.subjectFlexible dialogue trees
dc.subjectMental health support
dc.subjectMultilingual interaction
dc.subjectConversational AI
dc.titleFlexiDialogue: Integrating dialogue trees for mental health with large language modelseng
dc.typeconference object
dspace.entity.typePublication
oaire.awardNumberUIDB/50021/2020
oaire.awardTitleInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50021%2F2020/PT
oaire.citation.conferenceDate2025
oaire.citation.endPage275
oaire.citation.startPage268
oaire.citation.titleProceedings of the 11th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2025)
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameDias
person.givenNameJoão
person.identifier.ciencia-id541C-36A9-F1A0
person.identifier.orcid0000-0002-1653-1821
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublication66c729e3-99f2-4ddc-8da4-7c66e590800b
relation.isAuthorOfPublication.latestForDiscovery66c729e3-99f2-4ddc-8da4-7c66e590800b
relation.isProjectOfPublication0b14d63a-8f78-4e31-8a86-b72e1f07871f
relation.isProjectOfPublication.latestForDiscovery0b14d63a-8f78-4e31-8a86-b72e1f07871f

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