ArticleNature communications2025
LLM-generated messages can persuade humans on policy issues.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- On the conversational persuasiveness of GPT-4.Nature human behaviour · 2025Trial
- Conversational AI increases political knowledge as effectively as self-directed internet search.PNAS nexus · 2026Article
- How large language models judge and influence human cooperation.PNAS nexus · 2026Article
- Article
- Article
- Can AI help reduce prejudice? Evaluating the effectiveness of AI-powered personalized persuasion on support for transgender rights.PNAS nexus · 2026Article
- Article
- Persuading large language models to comply with objectionable requests.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- LLMs are ideological chameleons: personalized echo chambers in the Brazilian political context.Scientific reports · 2026Article
- Vaccine communication: a critical review of foundations, challenges, and future directions.NPJ vaccines · 2026Review
- Biased AI writing assistants shift users' attitudes on societal issues.Science advances · 2026Article
- Article
- AI models as consciousness attributors: how LLMs ascribe consciousness to other agents.Frontiers in psychology · 2026Article
- DeepSeek for healthcare: do no harm?AI and ethics · 2026Article
- Never say never: exploring the effects of knowledge availability on agent persuasiveness in controlled physiotherapy motivation dialogues.Frontiers in artificial intelligence · 2026Article
- How topic content shapes LLM personality-tailored persuasion: semantic anchoring and topic stereotype effects.Frontiers in psychiatry · 2026Article
- Chatbot Voting Advice Applications inform but seldom sway young unaligned voters.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A meta-analysis of the persuasive power of large language models.Scientific reports · 2025Article
- The persuasive potential of AI-paraphrased information at scale.PNAS nexus · 2025Article
- From digital traces to public vaccination behaviors: leveraging large language models for big data classification.Frontiers in artificial intelligence · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The emergence of large language models (LLMs) has made it possible for generative artificial intelligence (AI) to tackle many higher-order cognitive tasks, with critical implications for industry, government, and labor markets. Here, we investigate whether existing, openly-available LLMs can be used to create messages capable of influencing humans' political attitudes. Across three pre-registered experiments (total N = 4829), participants who read persuasive messages generated by LLMs showed significantly more attitude change across a range of policies - including polarized policies, like an assault weapons ban, a carbon tax, and a paid parental-leave program - relative to control condition participants who read a neutral message. Overall, LLM-generated messages were similarly effective in influencing policy attitudes as messages crafted by lay humans. Participants' reported perceptions of the authors of the persuasive messages suggest these effects occurred through somewhat distinct causal pathways. While the persuasiveness of LLM-generated messages was associated with perceptions that the author used more facts, evidence, logical reasoning, and a dispassionate voice, the persuasiveness of human-generated messages was associated with perceptions of the author as unique and original. These results demonstrate that recent developments in AI make it possible to create politically persuasive messages quickly, cheaply, and at massive scale.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.