A controlled eye-tracking study finds that code priority affects review time, cognitive load, and perceived quality but not reuse decisions, while author reputation changes visual attention patterns without altering performance or reuse choices.
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Two controlled experiments show multi-agent LLM configurations with both tutors and peers deliver higher learning gains and less homogeneous outputs than single-LLM tutoring in math problem-solving and essay writing.
A decision-theoretic model based on the observed Confirmation-Diagnosis-Correction-Redo user pattern places intermediate confirmations in AI agent tasks, yielding 81% user preference and 13.54% faster completion versus confirm-at-end.
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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An Eye for Trust: An Exploration of Developers' Trust Perceptions Through Urgency and Reputation
A controlled eye-tracking study finds that code priority affects review time, cognitive load, and perceived quality but not reuse decisions, while author reputation changes visual attention patterns without altering performance or reuse choices.
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Beyond the AI Tutor: Social Learning with LLM Agents
Two controlled experiments show multi-agent LLM configurations with both tutors and peers deliver higher learning gains and less homogeneous outputs than single-LLM tutoring in math problem-solving and essay writing.
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When Should Users Check? Modeling Confirmation Frequency inMulti-Step Agentic AI Tasks
A decision-theoretic model based on the observed Confirmation-Diagnosis-Correction-Redo user pattern places intermediate confirmations in AI agent tasks, yielding 81% user preference and 13.54% faster completion versus confirm-at-end.
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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.