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This paper investigates the effectiveness of AI-mediated dialogue in helping individuals recognize ableist microaggressions. The study compared four conditions: AI nudges toward bias, AI nudges toward inclusion, unguided dialogue, and a text-only control, measuring participants' ratings of scenarios and qualitative reflections. Results showed that dialogue-based interventions, particularly those with inclusive nudges, led to stronger recognition of ableism compared to the reading condition, while biased nudges were often rejected and increased overall negativity.
AI interventions designed to combat ableism can backfire, as biased nudges were often rejected and increased negativity, while inclusive nudges proved more effective as scaffolding for learning.
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.
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