Narrative-Centered Emotional Reflection: An Early Prototype for AI-Supported Emotional Self-Reflection

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.

AI emotional companions face a safety-rapport paradox: restrictive safeguards can damage supportive alliance, while permissive systems risk user harm. We present SLIP (Staged Layers of Intervention Protocol), a four-stage graduated methodology deriving interventions (none, soft, hard) from structured qualitative indicators -- affect intensity (a) and narrative dynamism (m) -- alongside ETHICS (Emergent Taxonomy for Human-AI Interaction Context Signals), a "signals not labels" taxonomy. An evaluation combining a small-scale production deployment (N=68 entries, 10 users, 10 weeks) with a synthetic persona battery (N=91, 5 behavioral-risk profiles) achieved 0% false positives for the flow persona and showed expected escalation patterns in crisis-oriented personas. However, initial results showed that 8 consecutive days of high-energy elevation produced zero interventions (0/8), exposing a boundary where the "do not pathologize" principle conflicts with safety. A subsequent three-model stress test demonstrated that increased model capability improves detection from 0/8 to 6/8 while preserving 0/10 flow false positives in the largest model. Read as preliminary, these findings position graduated intervention as a design direction for navigating -- not resolving -- the safety-rapport tension in affective computing.

Making high-stakes personal decisions involves cognitive, emotional, and intuitive processes, and individuals differ in how they allocate attention across these modes. Integration of these processes has shown to benefit decision making. Yet, most current decision-support systems focus primarily on supporting cognitive aspects, rather than adapting to the individual's thinking profile to support integration of different types of thoughts. In this study, we investigate an agent designed to encourage integration by adapting to the individual user's thought patterns. We explore its effects on participants' perceptions of the agent and their reflective behavior, in comparison with unaided pre-reflection and a baseline agent. In a between-subjects study (N = 128), our agent, which fostered broad and elaborated thinking, enabled more personalized reflective trajectories, elicited more integrative reflective language, and was perceived as providing stronger support for holistic reflection. In contrast, the baseline agent produced homogenized profiles dominated by cognitive language across participants.