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A. Cengaver
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How Concurrent Think-Aloud Data Reflects a User's Morality Trust in an Agent
A Think-Aloud Study of Morality Trust in Human-Agent Collaboration
Trust is a key factor in human-agent interaction, especially when users collaborate with an AI agent whose behavior is not fully predictable. Trust is often measured after an interaction using questionnaires, but these measures do not fully show how trust changes during the interaction or which agent behaviors shape the user's judgment. To address this limitation, this paper investigates whether Concurrent Think-Aloud (CTA) data can capture participants' real-time reasoning about the agent's moral trustworthiness during interaction. The study uses a modified version of the Moving Out game, in which participants collaborate with an AI agent while verbalizing their thoughts. Trust is examined using repeated in-game pop-up questionnaires, a post-interaction questionnaire, coded CTA transcripts, game logs, and screen recordings. The results show that morality-related trust is rarely expressed through formal moral terms such as "ethical'' or "principled''. Instead, participants express it through task-specific comments about whether the agent cooperates with them, ignores them, or acts in their interest. The findings also show that CTA markers align more closely with in-game trust ratings than with post-interaction questionnaire scores. Overall, the results suggest that CTA can complement questionnaire-based trust measures by capturing situated, real-time expressions of morality trust during interaction.
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Trust is a key factor in human-agent interaction, especially when users collaborate with an AI agent whose behavior is not fully predictable. Trust is often measured after an interaction using questionnaires, but these measures do not fully show how trust changes during the interaction or which agent behaviors shape the user's judgment. To address this limitation, this paper investigates whether Concurrent Think-Aloud (CTA) data can capture participants' real-time reasoning about the agent's moral trustworthiness during interaction. The study uses a modified version of the Moving Out game, in which participants collaborate with an AI agent while verbalizing their thoughts. Trust is examined using repeated in-game pop-up questionnaires, a post-interaction questionnaire, coded CTA transcripts, game logs, and screen recordings. The results show that morality-related trust is rarely expressed through formal moral terms such as "ethical'' or "principled''. Instead, participants express it through task-specific comments about whether the agent cooperates with them, ignores them, or acts in their interest. The findings also show that CTA markers align more closely with in-game trust ratings than with post-interaction questionnaire scores. Overall, the results suggest that CTA can complement questionnaire-based trust measures by capturing situated, real-time expressions of morality trust during interaction.