CA+: Cognition Augmented Counselor Agent Framework for Long-term Dynamic Client Engagement
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915216295460864 |
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| author | Tang, Yuanrong Kang, Yu Wang, Yifan Wang, Tianhong Zhong, Chen Gong, Jiangtao |
| author_facet | Tang, Yuanrong Kang, Yu Wang, Yifan Wang, Tianhong Zhong, Chen Gong, Jiangtao |
| contents | Current AI counseling systems struggle with maintaining effective long-term client engagement. Through formative research with counselors and a systematic literature review, we identified five key design considerations for AI counseling interactions. Based on these insights, we propose CA+, a Cognition Augmented counselor framework enhancing contextual understanding through three components:
(1) Therapy Strategies Module: Implements hierarchical Goals-Session-Action planning with bidirectional adaptation based on client feedback; (2) Communication Form Module: Orchestrates parallel guidance and empathy pathways for balanced therapeutic progress and emotional resonance; (3) Information Management: Utilizes client profile and therapeutic knowledge databases for dynamic, context-aware interventions.
A three-day longitudinal study with 24 clients demonstrates CA+'s significant improvements in client engagement, perceived empathy, and overall satisfaction compared to a baseline system. Besides, two licensed counselors confirm its high professionalism. Our research demonstrates the potential for enhancing LLM engagement in psychological counseling dialogues through cognitive theory, which may inspire further innovations in computational interaction in the future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21365 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CA+: Cognition Augmented Counselor Agent Framework for Long-term Dynamic Client Engagement Tang, Yuanrong Kang, Yu Wang, Yifan Wang, Tianhong Zhong, Chen Gong, Jiangtao Human-Computer Interaction Current AI counseling systems struggle with maintaining effective long-term client engagement. Through formative research with counselors and a systematic literature review, we identified five key design considerations for AI counseling interactions. Based on these insights, we propose CA+, a Cognition Augmented counselor framework enhancing contextual understanding through three components: (1) Therapy Strategies Module: Implements hierarchical Goals-Session-Action planning with bidirectional adaptation based on client feedback; (2) Communication Form Module: Orchestrates parallel guidance and empathy pathways for balanced therapeutic progress and emotional resonance; (3) Information Management: Utilizes client profile and therapeutic knowledge databases for dynamic, context-aware interventions. A three-day longitudinal study with 24 clients demonstrates CA+'s significant improvements in client engagement, perceived empathy, and overall satisfaction compared to a baseline system. Besides, two licensed counselors confirm its high professionalism. Our research demonstrates the potential for enhancing LLM engagement in psychological counseling dialogues through cognitive theory, which may inspire further innovations in computational interaction in the future. |
| title | CA+: Cognition Augmented Counselor Agent Framework for Long-term Dynamic Client Engagement |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2503.21365 |