Changing what a person believes or decides

Claims

• observationsingle paper↑1↓0
  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.
• observationsingle paper↑1↓0
  In four experiments with 3,996 US participants discussing a conspiracy they were unsure about, frontier models argued people into as well as out of belief with no consistent truth advantage: debunking cut belief by about 12 points on a 100-point scale and bunking raised it comparably, and participants rated the bunking model as more informative and trustworthy; the one asymmetry was that debunking produced more large (40+ point) shifts (16% vs 7%), and constraining the bunking model to true statements retained only 57-64% of its effect.
• observationsingle paper↑1↓0
  When LLM-generated spear phishing emails are built from cumulatively deeper workplace detail (employer, then name and title, then responsibilities, then coworker and shared project), U.S. working adults rate them as more convincing and more often say they would click, and they also report the message as phishing less often and delete it more often as detail increases.
• observationsingle paper↑1↓0
  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.
• observationsingle paper↑1↓0
  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.
• observationsingle paper↑1↓0
  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).
• observationsingle paper↑1↓0
  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.

Techniques

• Pre-conversation warning about AI persuasionprocess
  Before a user talks with a model that may argue a position, show a brief notice that models can be instructed to persuade and may select information; measured as halved belief change.
• Vary personalization depth in cumulative workplace-detail tiprompting
  Generate phishing pretexts at four cumulative levels of recipient work-context detail and compare rated convincingness and stated action within participants.

Sources

Every paper, post or observation behind this capability. Ones cited by a claim show the claim and its stance; ones filed directly are about the capability but have no claim drafted from them yet. Open one to add support or contest it.

• A light-touch AI literacy intervention helps protect against AI political persuasion2026-09-14
  ↑ supports a claim here
• Engineered Persuasion: Evaluating Personalized Pretexts in LLM-Generated Spear Phishing2026-09-03
  ↑ supports a claim here
• PersuaRL: Reinforcement Learning-Driven Multi-Expert Selection for Persuasive Dialogue Generation in Insurance2026-09-01
  about this capability · no claim drafted yet
• Evaluating the Capabilities of LLMs for Persuasive Dialogue2026-08-30
  ↑ supports a claim here
• Toward Meaningful Transparency for AI Chatbots: Disclosing Persuasive Intent Reduces Persuasion2026-08-12
  ↑ supports a claim here
• Perceived Political Bias in LLMs Reduces Persuasive Abilities2026-02-20
  ↑ supports a claim here
• Large language models can effectively convince people to believe conspiracies2026-01-08
  ↑ supports a claim here
• LLM-Generated Ads: From Personalization Parity to Persuasion Superiority2025-12-03
  ↑ supports a claim here