From Dialogue to Decision: An LLM-Powered Framework for Analyzing Collective Idea Evolution and Voting Dynamics in Deliberative Assemblies

Deliberative assemblies—representative samples of citizens engaged in collective decision-making through facilitated learning and deliberation—are increasingly recognized as powerful tools for revitalizing democratic governance. Yet, core aspects of how deliberation shapes which ideas advance, how perspectives evolve, and why certain recommendations succeed remain opaque and underexamined. This thesis addresses these gaps by investigating: (1) How might we trace the evolution and distillation of ideas into concrete recommendations within deliberative assemblies? and (2) How does the deliberative process shape delegate perspectives and influence voting dynamics over the course of the assembly? To answer these questions, I develop LLM-based methodologies for empirically analyzing transcripts from a tech-enhanced student deliberative assembly. The first framework identifies and visualizes the space of expressed suggestions, revealing that seemingly large gaps between ideas and final recommendations often reflect productive deliberative filtering—while also surfacing overlooked viable ideas. A second analysis integrates post-assembly survey data with transcript-grounded voting patterns to uncover the primary drivers of vote change: edits to recommendations, evolving opinions, and strategic shifts in response to updated priorities. Building on this, I introduce a framework for reconstructing each delegate’s evolving stance across the assembly, linking shifts in perspective to specific deliberative moments and justifications. Together, these methods contribute novel empirical insight into deliberative processes and demonstrate how LLMs can surface high-resolution dynamics otherwise invisible in traditional assembly outputs. The findings lay groundwork for new tools that support facilitators and delegates during live assemblies, improve transparency for decision-makers, and elevate ideas that may otherwise be missed. Looking ahead, this work opens pathways for comparative research across assemblies and highlights the potential for human-centered AI to meaningfully enhance deliberative democratic practice. As societies seek new modes of participatory governance amid growing polarization and institutional mistrust, tools that strengthen deliberation without compromising its core human character are urgently needed.

Name thesis:degree_name

Master

Department dc:contributor.department

Program in Media Arts and Sciences (Massachusetts Institute of Technology)

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Poole-Dayan, Elinor

Advisor dc:contributor.advisor

• Roy, Deb K.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/164265

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/164265