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초록

Existing counseling chatbots typically determine flow transitions based on explicit user profiling or emotion recognition. However, in real-world mental health contexts, users often struggle to accurately recognize or verbalize their internal states, making per-turn inference both costly and unreliable. We propose a selective modeling strategy that monitors deviations in the chatbot's own persona (so-called persona drift) as an indirect signal of user state change. When such deviation is detected by the heuristic module, signaling a subtle misalignment between the user's implicit state and the chatbot's consistent persona, the user state is reassessed and the counseling flow is conditionally adjusted. To reflect actual therapeutic processes, we implement a modular counseling system grounded in the Transtheoretical Model for Change (TTM), with five chatbots tailored according to user's behavior readiness stage. Together, the TTM-based architecture with persona drift module form a lightweight yet adaptive framework for tracking user state and guiding conversation flow in mental health chatbots.

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