Conversational AI is rapidly becoming a primary interface for information seeking and decision making, yet most systems still assume idealized users. In practice, human reasoning is bounded by limited attention, uneven knowledge, and reliance on heuristics that are adaptive but bias-prone. This article outlines a research pathway grounded in bounded rationality, and argues that conversational AI should be designed to work with human heuristics rather than against them. It identifies key directions for detecting cognitive vulnerability, supporting judgment under uncertainty, and evaluating conversational systems beyond factual accuracy, toward decision quality and cognitive robustness.

翻译：对话式人工智能正迅速成为信息检索与决策制定的主要交互界面，然而现有系统大多仍以理想化用户为前提。实践中，人类推理受限于注意力资源有限、知识储备不均以及依赖具有适应性但易产生偏见的启发式策略。本文基于有限理性理论提出研究路径，主张对话式人工智能的设计应顺应而非对抗人类启发式思维。研究明确了三个关键方向：认知脆弱性检测、不确定性下的判断支持，以及超越事实准确性、面向决策质量与认知鲁棒性的对话系统评估框架。