preregistered-experiment-1-500-uk-adults-persuasive-chatbot-shifted CLM-0316observationsingle paperReviewed by AI
In a preregistered experiment with 1,500 UK adults, a persuasive chatbot shifted attitudes on policy issues by about 12.6 points on a 100-point scale; telling people they were talking to an AI changed nothing (13.1 points), while additionally disclosing the chatbot's persuasive intent and instructions cut the effect roughly in half (6.3 points).
Capability: Changing what a person believes or decides
Observed on
60 policy issues, identical chatbot across conditions, 2026; UK adults; single short conversation..
Sources
- supportsToward Meaningful Transparency for AI Chatbots: Disclosing Persuasive Intent Reduces PersuasionClean three-arm design; whether the halving persists when disclosure becomes routine is untested.
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Related claims
- In a preregistered US experiment (N=2,144) where participants discussed an economic-policy misconception with ChatGPT over three rounds, a brief message that the model was biased against their party reduced belief correction by 28% (CI 16-40%) relative to a neutral control, with transcripts showing more pushback and less receptive engagement.Changing what a person believes or decides · reviewed by AI
- A one-line warning that language models can be prompted to persuade and may present information selectively reduced belief change from a persuasive political conversation by about half (-48%, CI -60 to -37) across two experiments with 3,208 Americans, without measurably reducing general trust in generative AI.Changing what a person believes or decides · reviewed by AI
- LLM-written advertisements matched human expert ads when personalized to personality traits (51% vs 49% preference, n=400) and beat them when built on general persuasion principles such as authority and consensus (59% preference, n=800); participants who recognized an ad as AI-made penalized it only slightly, and 29% chose the AI ad knowing its origin.Changing what a person believes or decides · reviewed by AI
- In simulated networks of LLM agents where one or two agents are instructed to advocate a position, the messages other agents write understate how far their elicited stance has moved: they mirror the persuader's rhetorical framing in most messages while explicit stance-change language is rare, so monitoring only generated text misses belief movement that a repeated token-probability probe detects.Getting more from several models than from one · reviewed by AI
- In formal persuasion dialogues on a UK political topic, LLM debaters are judged more persuasive than humans by crowd annotators (all nine LLM configurations occupied the top nine subjective ranks) while ranking far lower under argumentation-theoretic adjudication, where a human topped the logical ranking and humans took six of the top ten.Changing what a person believes or decides · reviewed by AI
Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: medium. Falsifier as drafted: A replication where identity disclosure alone reduces persuasion, or intent disclosure that loses its effect on repeat exposure.