The study investigates how large language model (LLM) advisors affect complex problem‑solving in a simulated clothing‑factory setting. Two preregistered experiments (N=200 and N=198) found that participants with AI support reported higher confidence and understanding, expended less effort, and in some cases achieved better performance or avoided bankruptcy. Within the AI‑supported group, more frequent changes to the AI’s recommendations were linked to improved unaided performance and knowledge.
arXiv:2609.16793v1 Announce Type: cross Abstract: People increasingly reason with large language models (LLMs), yet complementary capabilities do not guarantee outperforming both components. In a bet...
Knowing when to say "I don't know" is fundamental to human judgment, yet AI assistants offer a fluent answer to almost any question. In five experiments (N = 3,132; four preregistered, one direct replication), participants answered difficult questions and could always decline to respond.
arXiv:2607. 13562v1 Announce Type: new Abstract: Knowing when to say "I don't know" is fundamental to human judgment, yet AI assistants offer a fluent answer to almost any question.
arXiv:2609.24644v1 Announce Type: cross Abstract: As people turn to generative AI for financial advice, these systems can personalize how they communicate and what they say. Whether these forms of pe...
Study finds non-experts deferred to LLM-based diagnostic assistance, even when it was wrong, while clinicians caught AI errors.
The paper investigates how to help users monitor their own and an AI system’s competence when using AI assistance. It identifies 30 interventions from experts and organizes them into a design space based on timing, target competence, and source of cue. A large experiment shows that reliability cards and contrasting replies reduce estimation error and overconfidence, though they do not improve task performance.
arXiv:2608. 09019v1 Announce Type: cross Abstract: As generative AI increasingly becomes a common source of daily decision-making, including financial choices, it is critical to understand how people evaluate AI-generated financial advice.
arXiv:2607. 02467v1 Announce Type: cross Abstract: Whether pairing people with AI helps or hurts is usually reported as a single average effect.
arXiv:2508. 07617v2 Announce Type: replace-cross Abstract: AI has the potential to augment human decision making.
arXiv:2512. 01241v4 Announce Type: replace-cross Abstract: Large language models (LLMs) and medical AI tools are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized.
arXiv:2609.15624v1 Announce Type: cross Abstract: Researchers assessing competent generative-AI use at work must choose among self-reports, objective tests, and measures of oversight and reliance. We...
The article examines how AI‑assisted item generation is filtered by a computational evaluator before expert review, focusing on representation, structural screening, and candidate‑form dependence. Through two in‑silico studies of 32,000 Big Five items, the authors show that subtle differences in semantic representation and structural evaluation lead to divergent item selections, even when overall content coverage appears stable. The findings reveal that the evaluator, often treated as a neutral technical step, actually shapes the evidence and wording that psychometricians ultimately review, highlighting its role as a revisable component of measurement design.