TOPIC
Decision Support Tools
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Do reflection questions help people make better decisions with AI?
This explores whether conversational AI that prompts users to think through problems outperforms AI that simply provides answers. Understanding this matters for designing AI tools that genuinely improve human judgment rather than replace it.
Models can be trained to proactively identify missing information and request clarification AI passivity stems from next-turn reward optimization, not inability Conversational AI agents are structurally passive and reactive by design AI passivity in conversation stems from training incentives, not capability limits Insert-expansions from conversation analysis formalize when agents should probe users
What are the five specific conversation triggers where AI intervention adds value? How does AI assistance differ from search engines in cognitive impact? Does the timing of AI feedback relative to user reasoning change its effectiveness? Can users tell the difference between their own thinking and AI contribution? How do contrasting examples improve AI feedback quality over generic suggestions? What distinguishes reflection that satisfies constraints from reflection that merely sounds reflective? Why do conversational systems benefit from post-thinking between user turns? How does AI assistance affect human cognitive development over time? How does AI assistance change learning outcomes across different cognitive engagement levels? Can explicit reflection during AI-assisted work improve transfer of learning? Why do users prefer AI responses that actually harm their decision-making? How might automated evals eventually capture the human judgment designers exercise now?