ArticleScientific reports2024
The potential of generative AI for personalized persuasion at scale.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- On the conversational persuasiveness of GPT-4.Nature human behaviour · 2025Trial
- Introducing AI to an online petition platform changed outputs but not outcomes.Nature human behaviour · 2026Article
- Article
- Can AI help reduce prejudice? Evaluating the effectiveness of AI-powered personalized persuasion on support for transgender rights.PNAS nexus · 2026Article
- AI-assisted consent in paediatric medicine: ethical implications of using large language models to support decision-making.Journal of medical ethics · 2026Article
- Persuading large language models to comply with objectionable requests.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Large language models can detect verbal indicators of romantic attraction.Scientific reports · 2026Article
- Biased AI writing assistants shift users' attitudes on societal issues.Science advances · 2026Article
- Conditional effects of source expertise and pre-existing attitudes on objective knowledge in AI-assisted health information verification.Scientific reports · 2026Article
- Evaluation of large language models in nutrition risk screening: a comparative analysis across 8 LLMs based on real-world EHR datasets.BMC medical informatics and decision making · 2026Article
- How topic content shapes LLM personality-tailored persuasion: semantic anchoring and topic stereotype effects.Frontiers in psychiatry · 2026Article
- Never say never: exploring the effects of knowledge availability on agent persuasiveness in controlled physiotherapy motivation dialogues.Frontiers in artificial intelligence · 2026Article
- The impact of intention to adopt generative AI for exercise information on exercise adherence among Chinese college students: the mediating role of autonomous motivation and network analysis.Frontiers in public health · 2026Article
- Article
- Corpus-level behavioral intensity in human-AI conversations.Frontiers in artificial intelligence · 2026Article
- A meta-analysis of the persuasive power of large language models.Scientific reports · 2025Article
- Article
- The impact of advanced AI systems on democracy.Nature human behaviour · 2025Review
- Addressing Autonomy Risks in Generative Chatbots with the Socratic Method.Science and engineering ethics · 2025Article
- Augmenting research consent: should large language models (LLMs) be used for informed consent to clinical research?Research ethics · 2025Article
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6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Matching the language or content of a message to the psychological profile of its recipient (known as "personalized persuasion") is widely considered to be one of the most effective messaging strategies. We demonstrate that the rapid advances in large language models (LLMs), like ChatGPT, could accelerate this influence by making personalized persuasion scalable. Across four studies (consisting of seven sub-studies; total N = 1788), we show that personalized messages crafted by ChatGPT exhibit significantly more influence than non-personalized messages. This was true across different domains of persuasion (e.g., marketing of consumer products, political appeals for climate action), psychological profiles (e.g., personality traits, political ideology, moral foundations), and when only providing the LLM with a single, short prompt naming or describing the targeted psychological dimension. Thus, our findings are among the first to demonstrate the potential for LLMs to automate, and thereby scale, the use of personalized persuasion in ways that enhance its effectiveness and efficiency. We discuss the implications for researchers, practitioners, and the general public.
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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.