Helping the Helper: Supporting Peer Counselors via AI-Empowered Practice
  and Feedback
Shang-Ling Hsu Raj Sanjay Shah Prathik Senthil Zahra Ashktorab Casey Dugan Werner Geyer Diyi Yang
Millions of users come to online peer counseling platforms to seek support on
diverse topics ranging from relationship stress to anxiety. 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
of them often do not have systematic ways to receive guidelines or supervision.
In this work, we introduce CARE: an interactive AI-based tool to empower peer
counselors through automatic suggestion generation. During the practical
training stage, CARE helps diagnose which specific counseling strategies are
most suitable in the given context and provides tailored example responses as
suggestions. Counselors can choose to select, modify, or ignore any suggestion
before replying to the support seeker. Building upon the Motivational
Interviewing framework, CARE utilizes large-scale counseling conversation data
together with advanced natural language generation techniques to achieve these
functionalities. We demonstrate the efficacy of CARE by performing both
quantitative evaluations and qualitative user studies through simulated chats
and semi-structured interviews. We also find that CARE especially helps novice
counselors respond better in challenging situations.