Conversational AI increases political knowledge as effectively as self-directed internet search

3 citations

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, conversations with AI increase political knowledge (increase belief in true information and decrease belief in misinformation) to the same extent as self-directed internet search. Taken together, our results suggest that although people in the UK are increasingly turning to conversational AI for information about politics, this shift may not lead to increased public belief in political misinformation.

3
Citations
Survey
Paper Only
Relevant for

Study specs

A national survey (N=2,499) measured conversational AI usage for political information-seeking, followed by a series of randomised controlled trials (N=2,858) comparing conversational AI to self-directed internet search in improving political knowledge.

Sample Size
N=5,357
Study Type
experiment|survey
Year
2025
Human Data Platform
Prolific

Measured Outcomes

Extent of conversational AI usage for political knowledge-seeking in the UK and its efficacy in enhancing political knowledge and reducing misinformation compared to traditional internet searches.

Peer Review & Critical Discussion

3 threads

Potential Selection Bias in 2023 Cohort

DSJDr. Sarah J.
Verified PhD Candidate
12 replies

The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.

2 hours ago

Non-naive Participants Issue

MCM. Chen (OpenAI)
Data Scientist
8 replies

I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.

5 hours ago

RLHF Applicability to This Study Design

PRWProf. R. Williams
Verified Researcher
15 replies

The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.

1 day ago

Verify your expertise to join discussion

Create an account and verify your credentials to participate in peer discussions.