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arXiv Computation and Language By Yahan Li, Chaohao Du, Christopher Chun Kuizon, Zeyang Li, Nimra Ishfaq, Shupeng Cheng, Angelica Yinling Sun, Adam C. Frank, Angel Hsing-Chi Hwang, Ruishan Liu

CounselReflect: Opportunities and Challenges for Designing Tools to Support Self-Reflection on Mental Health and Well-Being Conversations with AI

Read the original on arXiv Computation and Language →

The paper introduces CounselReflect, a tool that converts counseling quality metrics into a framework for users to reflect on their mental‑health AI conversations. Through interviews with 21 users, the study finds that while most participants rarely reflect on their interactions, they identify specific questions they would like such a tool to address. The findings also reveal that users tend to confirm existing beliefs and focus on familiar dimensions, highlighting the need for reflection tools to expose blind spots and encourage a more comprehensive examination of AI interactions, especially when revisiting emotionally charged exchanges.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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