Divergent Paths to Depolarization: Dialogue Design Determines the Prosocial Benefits of AI-Assisted Political Argumentation

• URL: http://arxiv.org/abs/2605.23890v1
• Date: Fri, 22 May 2026 17:51:10 GMT
• Title: Divergent Paths to Depolarization: Dialogue Design Determines the Prosocial Benefits of AI-Assisted Political Argumentation
• Abstract summary: Argumentative dialogues across political divides can reduce polarization.<n>How the format of human-AI dialogues shapes their benefits remains unclear.<n>These findings highlight dialogue design as a key determinant of effective AI-mediated behavioral interventions.
• Score: 11.18468073756501
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Argumentative dialogues across political divides can reduce polarization, yet opportunities for citizens to engage with opposing views in accessible and structured ways remain limited. AI dialogue partners offer a scalable framework for such open-mindedness exercises, but how the format of human-AI dialogues shapes their benefits remains unclear. In a two-session online experiment, 469 US participants were assigned to argue either for or against their own attitude on a contested political issue with an AI chatbot. Our experimental findings show attitude-congruent dialogues produced greater immediate reduction in both affective and opinion polarization than attitude-incongruent dialogues. By contrast, attitude-incongruent dialogues elicited weaker cognitive state empathy than the non-AI reference task but increased cognitive trait empathy in the two-week period between sessions, suggesting the effects of active generation of attitude-incongruent arguments may emerge over time. These findings highlight dialogue design as a key determinant of effective AI-mediated behavioral interventions.

Related papers

• Challenging Partisan Expectations Reduces Political Polarization [35.59059755791736]
  Political conversations are often proposed as a remedy for political polarization, yet their effectiveness remains inconsistent.<n>We propose that conversations should reduce political polarization the most when they violate the expected link between partisan identity and issue position.<n>We find that expectation-challenging conversations in which participants talk with a disagreeing ingroup member or an agreeing outgroup member are effective in reducing affective and issue polarization.
  arXiv  Detail & Related papers  (2026-06-14T16:30:39Z)
• "I followed what felt right, not what I was told": Autonomy, Coaching, and Recognizing Bias Through AI-Mediated Dialogue [16.27769557566952]
  We present an experiment testing how AI-mediated dialogue influences recognition of ableism.<n>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)<n> 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.<n>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
  arXiv  Detail & Related papers  (2026-03-11T20:06:56Z)
• Reflecting Twice before Speaking with Empathy: Self-Reflective Alternating Inference for Empathy-Aware End-to-End Spoken Dialogue [53.95386201009769]
  We introduce EmpathyEval, a descriptive natural-language-based evaluation model for assessing empathetic quality in spoken dialogues.<n>We propose ReEmpathy, an end-to-end Spoken Language Models that enhances empathetic dialogue through a novel Empathetic Self-Reflective Alternating Inference mechanism.
  arXiv  Detail & Related papers  (2026-01-26T09:04:50Z)
• Aligning Spoken Dialogue Models from User Interactions [55.192134724622235]
  We propose a novel preference alignment framework to improve spoken dialogue models on realtime conversations from user interactions.<n>We create a dataset of more than 150,000 preference pairs from raw multi-turn speech conversations annotated with AI feedback.<n>Our findings shed light on the importance of a well-calibrated balance among various dynamics, crucial for natural real-time speech dialogue systems.
  arXiv  Detail & Related papers  (2025-06-26T16:45:20Z)
• Towards Dialogues for Joint Human-AI Reasoning and Value Alignment [3.432828370470386]
  We argue that enabling human-AI dialogue, purposed to support joint reasoning (i.e., 'inquiry'), is important for ensuring that AI decision making is aligned with human values and preferences.
  arXiv  Detail & Related papers  (2024-05-28T11:29:57Z)
• Sibyl: Empowering Empathetic Dialogue Generation in Large Language Models via Sensible and Visionary Commonsense Inference [40.96005200292604]
  We present an innovative framework named Sensible and Visionary Commonsense Knowledge (Sibyl)<n>It is designed to concentrate on the immediately succeeding dialogue, aiming to elicit more empathetic responses.<n> Experimental results demonstrate that incorporating our paradigm for acquiring commonsense knowledge into LLMs comprehensively enhances the quality of their responses.
  arXiv  Detail & Related papers  (2023-11-26T14:35:23Z)
• Opening up Minds with Argumentative Dialogues [14.903849413791443]
  argumentative dialogues aim to open up people's minds to help them become more understanding to views that are unfamiliar or in opposition to their own convictions. We present a dataset of 183 argumentative dialogues about 3 controversial topics: veganism, Brexit and COVID-19 vaccination. Open-mindedness is measured before and after engaging in the dialogue using a questionnaire from the psychology literature.
  arXiv  Detail & Related papers  (2023-01-16T12:47:16Z)
• DynaEval: Unifying Turn and Dialogue Level Evaluation [60.66883575106898]
  We propose DynaEval, a unified automatic evaluation framework. It is capable of performing turn-level evaluation, but also holistically considers the quality of the entire dialogue. Experiments show that DynaEval significantly outperforms the state-of-the-art dialogue coherence model.
  arXiv  Detail & Related papers  (2021-06-02T12:23:18Z)
• Is this Dialogue Coherent? Learning from Dialogue Acts and Entities [82.44143808977209]
  We create the Switchboard Coherence (SWBD-Coh) corpus, a dataset of human-human spoken dialogues annotated with turn coherence ratings. Our statistical analysis of the corpus indicates how turn coherence perception is affected by patterns of distribution of entities. We find that models combining both DA and entity information yield the best performances both for response selection and turn coherence rating.
  arXiv  Detail & Related papers  (2020-06-17T21:02:40Z)
• Rethinking Dialogue State Tracking with Reasoning [76.0991910623001]
  This paper proposes to track dialogue states gradually with reasoning over dialogue turns with the help of the back-end data. Empirical results demonstrate that our method significantly outperforms the state-of-the-art methods by 38.6% in terms of joint belief accuracy for MultiWOZ 2.1.
  arXiv  Detail & Related papers  (2020-05-27T02:05:33Z)
• You Impress Me: Dialogue Generation via Mutual Persona Perception [62.89449096369027]
  The research in cognitive science suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding.
  arXiv  Detail & Related papers  (2020-04-11T12:51:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.