Can Conversational AI loosen Us-Versus-Them Boundaries? The Effects of Common, Dual, and Separate Identity Framings on Pro-Immigrant Intergroup Helping

A preregistered experiment with 658 non-Latine White U.S. adults found that five rounds of dialogue with GPT-4o, an LLM, framing Latine immigrants in terms of a common or dual identity reduced separate categorization and increased willingness to act, though direct effects on behavior and pro-diversity beliefs were nonsignificant. The study, published on arXiv, suggests brief AI conversations can loosen us-versus-them boundaries but highlights the gap between cognitive recategorization and behavior.

arXiv:2608.19220v1 Announce Type: new Abstract: Rising immigration has intensified intergroup tensions in many countries. Traditional bias-reduction programs remain difficult to scale and increasingly constrained by U.S. policy. This preregistered experiment tested whether conversational AI can shift how majority-group members categorize and relate to Latine immigrants. Drawing on the common ingroup identity model, a quota-representative national sample of 658 non-Latine White U.S. adults completed five rounds of dialogue with a LLM GPT-4o . The model was instructed to frame Latine immigrants in terms of a common ingroup identity a shared American identity , a dual identity both Latine and American , or a separate identity distinct cultural boundaries , or to discuss an unrelated topic in a control condition. The manipulations altered categorization: relative to control, common ingroup identity and dual identity conversations lowered separate categorization, and dual identity conversations raised dual categorization. Although direct effects on behavior and pro-diversity beliefs were nonsignificant, willingness to act was significantly higher in the conditions emphasizing a superordinate identity common ingroup and dual identity . A path model further revealed indirect associations: both conditions reduced separate categorization, which in turn correlated with greater willingness to act. Semantic similarity analyses of the transcripts confirmed that conversations tracked their assigned narratives; participants' convergence with shared-identity language related positively, and with separate-identity language negatively, to willingness to act. These effects were largely consistent across moderators need for closure, openness to experience, and political orientation . The findings show that brief AI conversations can loosen us-versus-them boundaries while underscoring the gap between cognitive recategorization and behavior.