Watching AI Think: User Perceptions of Visible Thinking in Chatbots

TL;DR

This study investigates how visible thinking cues in chatbots influence user perceptions of empathy, warmth, and competence during help-seeking interactions, highlighting design considerations for transparent AI communication.

Contribution

It provides empirical insights into the effects of visible thinking displays on user perceptions in supportive chatbot interactions, informing better design practices.

Findings

Visible thinking cues increase perceived empathy and warmth.

Contextual factors modulate user perceptions of competence.

Reflections prior to responses enhance user engagement.

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.