Warning students about AI fallibility in an intelligent tutoring system increases their help-seeking behavior without reported changes in performance.
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Summary reasoning traces from LLMs maintain task performance and increase trust and appeal relative to answer-only or full-trace conditions, but none of the formats improve users' metacognitive calibration on reasoning tasks.
AI-labeled input devices raise user performance expectations but produce no measurable change in objective or subjective interaction outcomes.
citing papers explorer
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Warning About AI Fallibility Increases Help-Seeking in an Intelligent Tutoring System
Warning students about AI fallibility in an intelligent tutoring system increases their help-seeking behavior without reported changes in performance.
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Explaining Too Much? Understanding How Large Language Model Reasoning Traces Influence Performance and Metacognition
Summary reasoning traces from LLMs maintain task performance and increase trust and appeal relative to answer-only or full-trace conditions, but none of the formats improve users' metacognitive calibration on reasoning tasks.
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AI Washing Inflates Expected Performance but Not Interaction Outcomes: An AI Placebo Study Using Fitts' Law
AI-labeled input devices raise user performance expectations but produce no measurable change in objective or subjective interaction outcomes.