Trust in social LLM chatbots is a dynamic, situated user state that evolves through ongoing interactions rather than forming as a stable one-time judgment.
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Among novice programmers using AI code generators, trust did not predict compliance with suggestions, while performance correlated with both compliance and increased subsequent trust.
A literature review shows that constructs for appropriate reliance on AI are fragmented, presents three views on the topic, and calls for consensus on objective metrics to enable better comparisons across studies.
An empirical study creates guidelines for interpreting the Human-Computer Trust Scale as a starting point for assessing trust propensity in technology interactions, while stressing the need for contextual reflection.
citing papers explorer
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Trust as a Situated User State in Social LLM-Based Chatbots: A Longitudinal Study of Snapchat's My AI
Trust in social LLM chatbots is a dynamic, situated user state that evolves through ongoing interactions rather than forming as a stable one-time judgment.
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Relationships Between Trust, Compliance, and Performance for Novice Programmers Using AI Code Generation
Among novice programmers using AI code generators, trust did not predict compliance with suggestions, while performance correlated with both compliance and increased subsequent trust.
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From Trust to Appropriate Reliance: Measurement Constructs in Human-AI Decision-Making
A literature review shows that constructs for appropriate reliance on AI are fragmented, presents three views on the topic, and calls for consensus on objective metrics to enable better comparisons across studies.
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How Much Trust is Enough? Towards Calibrating Trust in Technology
An empirical study creates guidelines for interpreting the Human-Computer Trust Scale as a starting point for assessing trust propensity in technology interactions, while stressing the need for contextual reflection.
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