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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Adaptive Prompt Elicitation (APE) uses an information-theoretic framework to generate visual queries that elicit and compile user intent into better prompts for text-to-image models, showing improved alignment in benchmarks and a user study.
Two linked user studies find that LLM rationale correctness and certainty framing affect trust and decision confidence while presentation format does not, and incorrect rationales increase gaze attention and pupil size.
Pista decomposes AI agent actions in spreadsheets into auditable steps, enabling real-time user intervention that improves task outcomes, user comprehension, agent perception, and sense of co-ownership over baseline agents.
Literature review synthesizing evidence on user skepticism, verification, and reliance with hallucinating AI advisors, noting that output-related cues like warnings show weak effects and that content category has not been experimentally varied.
An online experiment finds that showing users an overview of an AI's values reduces reliance on AI suggestions during writing tasks.
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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Framing an AI with Values Reduces AI Reliance in AI-supported Writing Tasks
An online experiment finds that showing users an overview of an AI's values reduces reliance on AI suggestions during writing tasks.