Conversational AI increasingly supports everyday decision-making, yet most systems rely on data-centric reasoning rather than the heuristic and interactional strategies people use in natural conversation. To ground design in actual human practice, we analyze 955 real-world Korean conversations (15,476 utterances) involving food and travel decisions, applying a decision-making codebook through an LLM-assisted coding pipeline. Our findings reveal that people prioritize satisficing over optimization, relying heavily on internal knowledge and interactional strategies to manage cognitive load. Critically, we identify a frequency-efficiency mismatch: the most prevalent heuristics sustain conversational flow during exploration, whereas infrequent, rule-based strategies are highly effective at driving resolution during exploitation. By mapping how these patterns transfer across the spectrum of human-AI interaction, this work provides empirical grounding consistent with cognitive theories of decision-making and offers design implications that align AI systems with human heuristic processes.

翻译：对话式AI日益支持日常决策，但大多数系统依赖数据中心的推理而非人类在自然对话中使用的启发式和互动策略。为了将设计根植于真实人类实践，我们分析了涉及饮食和旅行决策的955个真实韩语对话（共15476个话语），通过LLM辅助编码流程应用决策编码手册。研究发现：人们倾向于满意化而非最优化，主要依赖内在知识和互动策略来管理认知负荷。关键在于，我们识别出频率-效率错配现象：最普遍的启发式策略在探索阶段维持对话流畅性，而不常见的基于规则的策略在利用阶段有效推动决议。通过映射这些模式在人机交互谱系中的迁移方式，本研究为与决策认知理论一致的实证基础提供了支撑，并提出了使AI系统与人类启发式过程对齐的设计启示。