People's decision-making abilities often fail to improve or may even erode when they rely on AI for decision-support, even when the AI provides informative explanations. We argue this is partly because people intuitively seek contrastive explanations, which clarify the difference between the AI's decision and their own reasoning, while most AI systems offer "unilateral" explanations that justify the AI's decision but do not account for users' thinking. To align human-AI knowledge on decision tasks, we introduce a framework for generating human-centered contrastive explanations that explain the difference between AI's choice and a predicted, likely human choice about the same task. Results from a large-scale experiment (N = 628) demonstrate that contrastive explanations significantly enhance users' independent decision-making skills compared to unilateral explanations, without sacrificing decision accuracy. Amid rising deskilling concerns, our research demonstrates that incorporating human reasoning into AI design can foster human skill development.

本研究解决了人们在依赖AI进行决策支持时，决策能力未能提升的实际问题。提出了一种以人为中心的对比解释框架，该框架阐明了AI决策与可能的人类选择之间的差异。实验结果表明，相较于单向解释，对比解释显著提高了用户的独立决策能力，而不影响决策准确性，进而促进了人类技能的发展。

能够预见人类误解的对比解释能改善人类决策能力