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https://doi.org/10.1145/371916...
Article . 2025 . Peer-reviewed
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https://dx.doi.org/10.48550/ar...
Article . 2025
License: arXiv Non-Exclusive Distribution
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Learn, Explore and Reflect by Chatting: Understanding the Value of an LLM-Based Voting Advice Application Chatbot

Authors: Jianlong Zhu; Manon Kempermann; Vikram Kamath Cannanure; Alexander Hartland; Rosa M. Navarrete; Giuseppe Carteny; Daniela Braun; +1 Authors

Learn, Explore and Reflect by Chatting: Understanding the Value of an LLM-Based Voting Advice Application Chatbot

Abstract

Voting advice applications (VAAs), which have become increasingly prominent in European elections, are seen as a successful tool for boosting electorates' political knowledge and engagement. However, VAAs' complex language and rigid presentation constrain their utility to less-sophisticated voters. While previous work enhanced VAAs' 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 VAA 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 VAA chatbot intuitive and informative, citing its simple language and flexible interaction. We further uncovered VAA chatbots' role as a catalyst for reflection and rationalization. Expanding on participants' desire for transparency, we provide design recommendations for building interactive and trustworthy VAA chatbots.

Accepted to ACM CUI 2025

Related Organizations
Keywords

ddc:004, FOS: Computer and information sciences, Computer Science - Computers and Society, Deliberation, Computers and Society (cs.CY), ddc:300, Computer Science - Human-Computer Interaction, Voting Advice Applications, Civic Education, Chatbot, Trustworthiness, Human-Computer Interaction (cs.HC)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green