Ableist microaggressions remain pervasive in everyday interactions, yet interventions to help people recognize them are limited. We present an experiment testing how AI-mediated dialogue influences recognition of ableism. 160 participants completed a pre-test, intervention, and a post-test across four conditions: AI nudges toward bias (Bias-Directed), inclusion (Neutral-Directed), unguided dialogue (Self-Directed), and a text-only non-dialogue (Reading). Participants rated scenarios on standardness of social experience and emotional impact; those in dialogue-based conditions also provided qualitative reflections. Quantitative results showed dialogue-based conditions produced stronger recognition than Reading, though trajectories diverged: biased nudges improved differentiation of bias from neutrality but increased overall negativity. Inclusive or no nudges remained more balanced, while Reading participants showed weaker gains and even declines. Qualitative findings revealed biased nudges were often rejected, while inclusive nudges were adopted as scaffolding. We contribute a validated vignette corpus, an AI-mediated intervention platform, and design implications highlighting trade-offs conversational systems face when integrating bias-related nudges.

翻译：能力歧视微侵犯在日常互动中依然普遍存在，但帮助人们识别此类行为的干预措施却十分有限。本研究通过实验检验AI中介对话如何影响对能力歧视的识别。160名参与者完成了前测、干预与后测，实验设置四种条件：AI引导偏向偏见（偏见导向）、引导包容（中立导向）、无引导对话（自主导向）以及纯文本非对话阅读（阅读对照）。参与者对社会体验的常规性及情感影响进行场景评分；基于对话条件的参与者还提供了质性反思。定量结果显示，基于对话的条件比阅读对照产生更强的识别效果，但发展轨迹出现分化：偏见引导能提升对偏见与中立情境的区分度，却增加了整体负面评价；包容性或无引导条件则保持更平衡的态势，而阅读对照参与者的改善较弱甚至出现倒退。质性研究发现，偏见引导常遭参与者排斥，而包容性引导则被采纳为认知支架。本研究贡献包括：一套经过验证的叙事情境语料库、一个AI中介干预平台，以及揭示对话系统整合偏见相关引导时面临权衡的设计启示。