Large Language Model-Powered Conversational Agent Delivering Problem-Solving Therapy (PST) for Family Caregivers: Enhancing Empathy and Therapeutic Alliance Using In-Context Learning
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916792645976064 |
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| author | Wang, Liying D., Ph. Carrington, Daffodil S., M. Filienko, Daniil S., M. Jazmi, Caroline El S., M. Xie, Serena Jinchen S., M. De Cock, Martine D., Ph. Iribarren, Sarah D., Ph. Yuwen, Weichao D, Ph. |
| author_facet | Wang, Liying D., Ph. Carrington, Daffodil S., M. Filienko, Daniil S., M. Jazmi, Caroline El S., M. Xie, Serena Jinchen S., M. De Cock, Martine D., Ph. Iribarren, Sarah D., Ph. Yuwen, Weichao D, Ph. |
| contents | Family caregivers often face substantial mental health challenges due to their multifaceted roles and limited resources. This study explored the potential of a large language model (LLM)-powered conversational agent to deliver evidence-based mental health support for caregivers, specifically Problem-Solving Therapy (PST) integrated with Motivational Interviewing (MI) and Behavioral Chain Analysis (BCA). A within-subject experiment was conducted with 28 caregivers interacting with four LLM configurations to evaluate empathy and therapeutic alliance. The best-performing models incorporated Few-Shot and Retrieval-Augmented Generation (RAG) prompting techniques, alongside clinician-curated examples. The models showed improved contextual understanding and personalized support, as reflected by qualitative responses and quantitative ratings on perceived empathy and therapeutic alliances. Participants valued the model's ability to validate emotions, explore unexpressed feelings, and provide actionable strategies. However, balancing thorough assessment with efficient advice delivery remains a challenge. This work highlights the potential of LLMs in delivering empathetic and tailored support for family caregivers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11376 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large Language Model-Powered Conversational Agent Delivering Problem-Solving Therapy (PST) for Family Caregivers: Enhancing Empathy and Therapeutic Alliance Using In-Context Learning Wang, Liying D., Ph. Carrington, Daffodil S., M. Filienko, Daniil S., M. Jazmi, Caroline El S., M. Xie, Serena Jinchen S., M. De Cock, Martine D., Ph. Iribarren, Sarah D., Ph. Yuwen, Weichao D, Ph. Artificial Intelligence Computation and Language Human-Computer Interaction Family caregivers often face substantial mental health challenges due to their multifaceted roles and limited resources. This study explored the potential of a large language model (LLM)-powered conversational agent to deliver evidence-based mental health support for caregivers, specifically Problem-Solving Therapy (PST) integrated with Motivational Interviewing (MI) and Behavioral Chain Analysis (BCA). A within-subject experiment was conducted with 28 caregivers interacting with four LLM configurations to evaluate empathy and therapeutic alliance. The best-performing models incorporated Few-Shot and Retrieval-Augmented Generation (RAG) prompting techniques, alongside clinician-curated examples. The models showed improved contextual understanding and personalized support, as reflected by qualitative responses and quantitative ratings on perceived empathy and therapeutic alliances. Participants valued the model's ability to validate emotions, explore unexpressed feelings, and provide actionable strategies. However, balancing thorough assessment with efficient advice delivery remains a challenge. This work highlights the potential of LLMs in delivering empathetic and tailored support for family caregivers. |
| title | Large Language Model-Powered Conversational Agent Delivering Problem-Solving Therapy (PST) for Family Caregivers: Enhancing Empathy and Therapeutic Alliance Using In-Context Learning |
| topic | Artificial Intelligence Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2506.11376 |