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

Published in ACM Conference on Conversational User Interfaces 2025 (CUI ’25), 2025

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.

Recommended citation: Jianlong Zhu, Manon Kempermann, Vikram Kamath Cannanure, Alexander Hartland, Rosa M. Navarrete, Giuseppe Carteny, Daniela Braun, and Ingmar Weber. 2025. Learn, Explore and Reflect by Chatting: Understanding the Value of an LLM-Based Voting Advice Application Chatbot. In Proceedings of the 7th ACM Conference on Conversational User Interfaces (CUI ’25), July 8–10, 2025, Waterloo, ON, Canada. ACM, New York, NY, USA, 22 pages. https://doi.org/10.1145/3719160.3736611
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Elements of Data Science and AI

Undergraduate course, Saarland University, Department of Computer Science, 2024

As a teaching assistant, I designed course materials and oversaw the exam administration for the undergraduate lecture “Elements of Data Science and AI”.