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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.
arXiv:2509.04183v3 Announce Type: replace-cross Abstract: The growing demand for scalable psychological counseling highlights the need for high-quality, privacy-compliant data, yet such data remains...
arXiv:2602. 18443v2 Announce Type: replace-cross Abstract: Psychosocial online counselling frequently encounters generic subject lines that impede efficient case prioritisation.
While AI-assisted text-based counseling is gaining attention, it remains empirically unclear which counselor behaviors are associated with higher dialogue quality. Existing research often focuses heav...
arXiv:2511.11689v4 Announce Type: replace-cross Abstract: Generative AI chatbots built for mental health could extend access to care, but evidence from real-world use is limited. We report a single-a...
The paper investigates which counselor behaviors correlate with higher dialogue quality in AI-assisted text-based counseling. Using the KokoroChat dataset, the authors find that the strategy of affirmation consistently associates with better session quality, more so than reflection. Cross-dataset experiments suggest this signal also appears, to some extent, in an English dataset of non-expert supporters.