Deliberative democratic theory suggests that civic competence: the capacity to navigate disagreement, weigh competing values, and arrive at collective decisions is not innate but developed through practice. Yet opportunities to cultivate these skills remain limited, as traditional deliberative processes like citizens' assemblies reach only a small fraction of the population. We present Agora, an AI-powered platform that uses LLMs to organize authentic human voices on policy issues, helping users build consensus-finding skills by proposing and revising policy recommendations, hearing supporting and opposing perspectives, and receiving feedback on how policy changes affect predicted support. In a preliminary study with 44 university students, access to the full interface with voice explanations, as opposed to aggregate support distributions alone, significantly improved self-reported perspective-taking and the extent to which statements acknowledged multiple viewpoints. These findings point toward a promising direction for scaling civic education.

翻译：协商民主理论认为，公民能力——即处理分歧、权衡竞争性价值、达成集体决策的能力——并非与生俱来，而是通过实践培养形成的。然而，培养这些技能的机会仍然有限，因为传统的协商程序（如公民大会）仅能覆盖极小部分人群。我们提出Agora这一基于人工智能的平台，利用大语言模型整理政策议题中真实的人类声音，通过帮助用户提出并修订政策建议、听取支持与反对观点、接收政策变更对预测支持率影响的反馈，从而培养其达成共识的技能。在面向44名大学生的初步研究中，相较于仅展示综合支持分布，完整界面配合语音解释的交互方式显著提升了用户自我报告的观点采择能力，以及陈述中对多元视角的认可程度。这些发现为规模化推进公民教育指明了有前景的发展方向。