Mixed-methods study finds AI assistance linked to higher textual overlap with suggestions in writing tasks, and a reflective interface prototype increases user awareness of AI incorporation.
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LLM reasoning traces and post-hoc explanations increase false trust in incorrect predictions, whereas contrastive dual explanations enhance users' ability to distinguish correct from incorrect AI outputs.
Human-AI hybrids achieve only +0.4pp over AI alone on diverse tasks because confidence routing fails to identify the small set of cases where humans can correct AI errors.
Structural mental models of AI writing assistants improve system understanding and usability but result in more grammatical errors in user writing compared to functional models.
A survey-experiment with 236 participants shows most believe myths about gig worker vulnerabilities and that targeted counterarguments can reduce those beliefs.
citing papers explorer
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Overreliance in Writing Tasks: Exploring Similarity-Based Measures of AI Influence on Writing and Proposing a Reflective Writing Interface Intervention
Mixed-methods study finds AI assistance linked to higher textual overlap with suggestions in writing tasks, and a reflective interface prototype increases user awareness of AI incorporation.
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Evaluating the False Trust Engendered by LLM Explanations
LLM reasoning traces and post-hoc explanations increase false trust in incorrect predictions, whereas contrastive dual explanations enhance users' ability to distinguish correct from incorrect AI outputs.
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Toward Human-AI Complementarity Across Diverse Tasks
Human-AI hybrids achieve only +0.4pp over AI alone on diverse tasks because confidence routing fails to identify the small set of cases where humans can correct AI errors.
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From Use to Oversight: How Mental Models Influence User Behavior and Output in AI Writing Assistants
Structural mental models of AI writing assistants improve system understanding and usability but result in more grammatical errors in user writing compared to functional models.
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Understanding, Challenging, and Demystifying Perceptions of Gig Worker Vulnerabilities
A survey-experiment with 236 participants shows most believe myths about gig worker vulnerabilities and that targeted counterarguments can reduce those beliefs.