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Agora: Teaching the Skill of Consensus-Finding with AI Personas Grounded in Human Voice

Suyash Fulay, Prerna Ravi, Emily Kubin, Shrestha Mohanty, Michiel Bakker, Deb Roy

TL;DR

Agora is 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.

Abstract

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.

Agora: Teaching the Skill of Consensus-Finding with AI Personas Grounded in Human Voice

TL;DR

Agora is 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.

Abstract

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.
Paper Structure (12 sections, 3 figures)

This paper contains 12 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Background: HCI and Deliberation
  3. Tool Description
  4. Data Collection: AI-Conducted Interviews
  5. Agora User Interface
  6. Backend Implementation
  7. Predicting Policy Support
  8. Generating Audio Medleys
  9. Dynamic Feedback Loop
  10. Experimental Setup and Evaluation
  11. Results
  12. Discussion

Figures (3)

  • Figure 1: Full Agora interface for treatment condition. Top image A shows how participants iterate and test their policies, Bottom image B shows the profile view participants see when clicking on the different avatars.
  • Figure 2: Agora interface for control condition
  • Figure 3: Agora learning outcomes and consensus quality.