Novice Reliance Calibration in AI-Assisted Decision Making: The Role of Explanations and Self-Assessment
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arXiv:2606. 06081v1 Announce Type: new Abstract: Appropriate reliance on AI advice has become a central research theme in human-AI collaboration.
The study investigates how different AI support formats influence human decision-making across two tasks: abstract visual reasoning with RAVEN matrices and deductive logical reasoning with LSAT problems. Findings reveal that in visual reasoning, predictions alone and predicted probabilities best support accuracy and error recovery, while in logical reasoning, LLM explanations outperform other supports. The results suggest that effective human–AI collaboration requires task‑specific support strategies rather than a one‑size‑fits‑all approach.
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:2604. 04721v3 Announce Type: replace Abstract: People often optimize for long-term goals in collaboration: A mentor or companion doesn't just answer questions, but also scaffolds learning, tracks progress, and prioritizes the other person's growth over immediate results.
arXiv:2604. 01114v3 Announce Type: replace-cross Abstract: As generative AI systems are integrated into educational settings, students often encounter AI-generated output while working through learning tasks, either by requesting help or through integrated tools.
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.