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 early-stage 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, participants using the full interface (with access to voice explanations) reported higher levels of problem-solving skills, internal deliberation, and produced higher quality consensus statements compared to a control condition showing only aggregate support distributions. These initial 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、权衡竞争性价值并达成集体决策的能力——并非与生俱来，而是通过实践培养的。然而，培养这些技能的机会仍然有限，因为像公民大会这样的传统协商过程只能覆盖一小部分人群。我们提出了阿戈拉（Agora），一个早期的人工智能驱动平台，该平台利用大型语言模型（LLMs）来组织关于政策议题的真实人类声音，通过提出和修改政策建议、听取支持和反对的观点，以及接收关于政策变化如何影响预测支持度的反馈，帮助用户培养共识寻找技能。在一项涉及44名大学生的初步研究中，与仅显示聚合支持分布的控制条件相比，使用完整界面（可访问语音解释）的参与者报告了更高水平的问题解决能力、内部协商度，并产生了更高质量的共识声明。这些初步发现为扩大公民教育规模指出了一个有前景的方向。