Watching AI Think: User Perceptions of Visible Thinking in Chatbots

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Abstract
People increasingly turn to conversational agents such as ChatGPT to seek guidance for their personal problems. As these systems grow in capability, many now display elements of "thinking": short reflective statements that reveal a model's intentions or values before responding. While initially introduced to promote transparency, such visible thinking can also anthropomorphise the agent and shape user expectations. Yet little is known about how these displays affect user perceptions in help-seeking contexts. We conducted a 3 x 2 mixed design experiment examining the impact of 'Thinking Content' (None, Emotionally-Supportive, Expertise-Supportive) and 'Conversation Context' (Habit-related vs. Feelings-related problems) on users' perceptions of empathy, warmth, competence, and engagement. Participants interacted with a chatbot that either showed no visible thinking or presented value-oriented reflections prior to its response. Our findings contribute to understanding how thinking transparency influences user experience in supportive dialogues, and offer implications for designing conversational agents that communicate intentions in sensitive, help-seeking scenarios.
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