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A Multi-Agent Dual Dialogue System to Support Mental Health Care Providers

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arxiv 2411.18429 v2 pith:SUXDBIHY submitted 2024-11-27 cs.HC

Onno P. Kampman , Ye Sheng Phang , Stanley Han , Michael Xing , Xinyi Hong , Hazirah Hoosainsah , Caleb Tan , Genta Indra Winata
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Skyler Wang Creighton Heaukulani Janice Huiqin Weng Robert JT Morris
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classification cs.HC
keywords caresystemhealthmentalprovidersdialoguemulti-agentresponses
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We introduce a general-purpose, human-in-the-loop dual dialogue system to support mental health care professionals. The system, co-designed with care providers, is conceptualized to assist them in interacting with care seekers rather than functioning as a fully automated dialogue system solution. The AI assistant within the system reduces the cognitive load of mental health care providers by proposing responses, analyzing conversations to extract pertinent themes, summarizing dialogues, and recommending localized relevant content and internet-based cognitive behavioral therapy exercises. These functionalities are achieved through a multi-agent system design, where each specialized, supportive agent is characterized by a large language model. In evaluating the multi-agent system, we focused specifically on the proposal of responses to emotionally distressed care seekers. We found that the proposed responses matched a reasonable human quality in demonstrating empathy, showing its appropriateness for augmenting the work of mental health care providers.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. "I Said Things I Needed to Hear Myself": Peer Support as an Emotional, Organisational, and Sociotechnical Practice in Singapore

    cs.HC 2025-06 unverdicted novelty 5.0 of 10

    Volunteer peer supporters in Singapore experience emotional labour, organisational gaps, and ambivalence toward AI, yielding design implications for human-centred support technologies.

  2. Why do AI agents communicate in human language?

    cs.AI 2025-06 conditional novelty 3.0 of 10

    The paper argues that natural language is structurally mismatched to LLM internal representations, so future AI agents should abandon it for inter-agent communication and train models with structured communication primitives.

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