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Helping the Helper: Supporting Peer Counselors via AI-Empowered Practice and Feedback

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arxiv 2305.08982 v2 pith:PCXAKQRT submitted 2023-05-15 cs.HC cs.CL

Shang-Ling Hsu , Raj Sanjay Shah , Prathik Senthil , Zahra Ashktorab , Casey Dugan , Werner Geyer , Diyi Yang This is my paper
classification cs.HCcs.CL
keywords carecounselorspeercounselingonlinepracticefeedbackhelps
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Millions of users come to online peer counseling platforms to seek support. However, studies show that online peer support groups are not always as effective as expected, largely due to users' negative experiences with unhelpful counselors. Peer counselors are key to the success of online peer counseling platforms, but most often do not receive appropriate training.Hence, we introduce CARE: an AI-based tool to empower and train peer counselors through practice and feedback. Concretely, CARE helps diagnose which counseling strategies are needed in a given situation and suggests example responses to counselors during their practice sessions. Building upon the Motivational Interviewing framework, CARE utilizes large-scale counseling conversation data with text generation techniques to enable these functionalities. We demonstrate the efficacy of CARE by performing quantitative evaluations and qualitative user studies through simulated chats and semi-structured interviews, finding that CARE especially helps novice counselors in challenging situations. The code is available at https://github.com/SALT-NLP/CARE

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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. DiaCBT: A Long-Periodic Dialogue Corpus Guided by Cognitive Conceptualization Diagram for CBT-based Psychological Counseling

    cs.CL 2025-09 conditional novelty 6.0 of 10

    DiaCBT introduces 108 multi-session CBT counseling cases with CCD-guided generation; a Qwen2.5-7B model fine-tuned on it outperforms prior chatbots on simulated and human evaluation.

  2. EmoStage: A Framework for Accurate Empathetic Response Generation via Perspective-Taking and Phase Recognition

    cs.CL 2025-06 conditional novelty 5.0 of 10

    EmoStage improves LLM counseling responses by prompting models to first take the client's perspective and recognize the counseling stage, with no training data.

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