Mar 31, 2026, cs.CLJ/K move · Enter open · S save
Yahan Li, Chaohao Du, Christopher Chun Kuizon, Zeyang Li+6
University of Southern California, United States · The University of Texas at Austin, Austin, Texas, United States · Department of Psychiatry and Behavioral Sciences, University of Southern California, Los Angeles, California, United States
AI is increasingly used for mental health and well-being support, creating an urgent need for safer engagement, while design, evaluation, and governance take time to develop. We explore a complementary approach: helping users critically reflect on their own AI conversations. We introduce CounselReflect, a tool that translates literature-grounded counseling quality metrics into a user-facing reflection framework. Using CounselReflect as a study probe, we interviewed 21 users of AI for mental health and well-being support. Although most participants did not routinely reflect on their conversations, they articulated concrete questions they would want reflection to address. Tool-assisted reflection also revealed challenges: participants selectively sought evidence confirming existing perceptions of AI and prioritized dimensions they already valued. We argue that reflection tools should surface blind spots and scaffold more holistic examination of AI interactions. Finally, overcoming emotional barriers to revisiting tense conversations remains a major design challenge and warrants input from future work.