Conversational AI increases political knowledge as effectively as self-directed internet search
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Abstract (arXiv)
Conversational AI systems are increasingly being used in place of traditional search engines to help users complete information-seeking tasks. This has raised concerns in the political domain, where biased or hallucinated outputs could misinform voters or distort public opinion. However, in spite of these concerns, the extent to which conversational AI is used for political information-seeking, as well the potential impact of this use on users' political knowledge, remains uncertain. Here, we address these questions: First, in a representative national survey of the UK public (N = 2,499), we find that in the week before the 2024 election as many as 32% of chatbot users - and 13% of eligible UK voters - have used conversational AI to seek political information relevant to their electoral choice. Second, in a series of randomised controlled trials (N = 2,858 total) we find that across issues, models, and prompting strategies, task-directed conversations with AI to research specific political topics increase political knowledge (increase belief in true information and decrease belief in misinformation) to the same extent as self-directed Google search. Taken together, our results suggest that people in the UK are increasingly turning to conversational AI for information about politics. These findings substantially extend prior work by demonstrating that conversational AI's effects on political knowledge generalise across multiple topics, political perspectives, and model families, suggesting that the shift toward AI-assisted political information-seeking may not lead to increased public belief in political misinformation.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Sep 2025.
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Citing papers per year: 2025: 3; 2026: 5.
8 papers.
- 2025What Is The Political Content in LLMs' Pre- and Post-Training Data?cited by 7
- 2025From Noise to Signal to Selbstzweck: Reframing Human Label Variation in the Era of Post-training in NLPcited by 3
- 2025Follow My Lead: Logical Fallacy Classification with Knowledge-Augmented LLMscited by 1
- 2026DeliberationBench: A Normative Benchmark for the Influence of Large Language Models on Users' Viewscited by 1
- 2026Overreliance on AI in Information-seeking from Video Contentcited by 1
- 2026Polistemics: Evaluating LLMs as Information Mediators in Politics & Electionscited by 0
- 2026Position: The Alignment Community is Unintentionally Building a Censor's Toolkitcited by 0
- 2026WildSEEK: Evaluating Language Models for Information-Seekingcited by 0
- 2026WildSEEK: Evaluating Language Models for Information-Seeking31 Aug 2026
- 2026Polistemics: Evaluating LLMs as Information Mediators in Politics & Elections28 Jul 2026
- 2026Position: The Alignment Community is Unintentionally Building a Censor's Toolkit5 Jun 2026
- 2026Overreliance on AI in Information-seeking from Video Content20 Mar 2026
- 2026DeliberationBench: A Normative Benchmark for the Influence of Large Language Models on Users' Views22 Feb 2026
- 2025Follow My Lead: Logical Fallacy Classification with Knowledge-Augmented LLMs11 Oct 2025
- 2025From Noise to Signal to Selbstzweck: Reframing Human Label Variation in the Era of Post-training in NLP9 Oct 2025
- 2025What Is The Political Content in LLMs' Pre- and Post-Training Data?26 Sep 2025