Helping the Helper: Supporting Peer Counselors via AI-Empowered Practice and Feedback
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866909535873007616 |
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| author | Hsu, Shang-Ling Shah, Raj Sanjay Senthil, Prathik Ashktorab, Zahra Dugan, Casey Geyer, Werner Yang, Diyi |
| author_facet | Hsu, Shang-Ling Shah, Raj Sanjay Senthil, Prathik Ashktorab, Zahra Dugan, Casey Geyer, Werner Yang, Diyi |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_08982 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Helping the Helper: Supporting Peer Counselors via AI-Empowered Practice and Feedback Hsu, Shang-Ling Shah, Raj Sanjay Senthil, Prathik Ashktorab, Zahra Dugan, Casey Geyer, Werner Yang, Diyi Human-Computer Interaction Computation and Language 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 |
| title | Helping the Helper: Supporting Peer Counselors via AI-Empowered Practice and Feedback |
| topic | Human-Computer Interaction Computation and Language |
| url | https://arxiv.org/abs/2305.08982 |