Conversational AI · 2025
Learn, Explore and Reflect by Chatting: Understanding the Value of an LLM-Based Voting Advice Application Chatbot
Proceedings of the 7th ACM Conference on Conversational User Interfaces (CUI).
About the research
Abstract
Voting advice applications, which have become increasingly prominent in European elections, are seen as a successful tool for boosting electorates’ political knowledge and engagement. However, their complex language and rigid presentation constrain their utility to less-sophisticated voters. While previous work enhanced click-based interaction with scripted explanations, a conversational chatbot’s potential for tailored discussion and deliberate political decision-making remains untapped. Our exploratory mixed-method study investigates how LLM-based chatbots can support voting preparation. We deployed a voting advice chatbot to 331 users before Germany’s 2024 European Parliament election, gathering insights from surveys, conversation logs, and 10 follow-up interviews. Participants found the chatbot intuitive and informative, citing its simple language and flexible interaction. We further uncovered its role as a catalyst for reflection and rationalization. Expanding on participants’ desire for transparency, we provide design recommendations for building interactive and trustworthy voting advice chatbots.
Citation
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@inproceedings{zhu2025learn,
title = {Learn, Explore and Reflect by Chatting: Understanding the Value of an LLM-Based Voting Advice Application Chatbot},
author = {Zhu, Jianlong and Kempermann, Manon and Cannanure, Vikram Kamath and Hartland, Alexander and Navarrete, Rosa M. and Carteny, Giuseppe and Braun, Daniela and Weber, Ingmar},
booktitle = {Proceedings of the 7th ACM Conference on Conversational User Interfaces},
year = {2025},
doi = {10.1145/3719160.3736611}
}