Large longitudinal RCT finds high rates of following AI personal advice but no sustained well-being gains versus a hobbies control condition.
Conversational AI increases political knowledge as effectively as self-directed internet search
2 Pith papers cite this work. Polarity classification is still indexing.
abstract
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.
years
2025 2representative citing papers
Training data for open LLMs is systematically left-leaning, with pre-training corpora containing more political material than post-training data and model stances aligning with data distributions.
citing papers explorer
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People readily follow personal advice from AI but it does not improve their well-being
Large longitudinal RCT finds high rates of following AI personal advice but no sustained well-being gains versus a hobbies control condition.
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What Is The Political Content in LLMs' Pre- and Post-Training Data?
Training data for open LLMs is systematically left-leaning, with pre-training corpora containing more political material than post-training data and model stances aligning with data distributions.