Enhancing Online Deliberation Through AI-Driven Counter-Attitudinal Interventions: Direct and Indirect Effects on Users’ Perceptions and Behavior

Abstract

This study tests whether an AI-based counter-attitudinal comment recommender (AI-CAR) enhances participants’ subjective deliberative experiences and reduces incivility by restructuring the visibility of dissenting viewpoints. We analyzed three-wave data from 827 participants in a 10-day simulated digital citizens’ council in Germany. Participants discussed one of two political issues asynchronously and were randomly assigned to one of three conditions: counter-attitudinal AI recommendations, randomly selected recommendations, or no recommendations. Assignment to AI-CAR increased perceived equality of viewpoints. Higher perceived equality at wave 2 was associated with greater internal deliberation and discussion satisfaction at wave 3, controlling for their wave-2 levels. The corresponding model-based indirect effects were statistically significant, whereas comparable indirect effects were not statistically significant for randomly selected recommendations. AI-CAR did not reduce incivility in participants’ contributions. Overall, the findings highlight the potential of AI-driven structural curation to support subjective deliberative experiences, while effects on incivility remain limited.