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The potential of generative AI for personalized persuasion at scale
Open accessAbstract
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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“AI chatbots can craft hyper-tailored messages using psychographic and behavioral data (Costello, Pennycook, and Rand 2024; Matz et al. 2024)”
From Emotional Manipulation by AI Companions (2025) · cites this paper as Matz et al. 2024
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“On the other hand, LLMs can influence and persuade humans26, with research demonstrating that LLM-based writing assistants are capable of swaying people’s views27.”
From Generative language models exhibit social identity biases (2024) · cites this paper as [26]
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“LLMs are advanced generative AI systems that use transformer neural network architectures 36 to learn language representations from vast corpora of text data.”
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MethodAn Introduction to the Five‐Factor Model and Its Applications
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BackgroundMethodLLaMA: Open and Efficient Foundation Language Models
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Background
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Understanding Consumer Responses and Campaign Productivity in Generative AI-Powered Advertising
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“Generative AI has also been shown to be effective at mimicking certain psychological conditions [11], and could be useful for representing those conditions accurately in synthetic datasets.”
Influencing Minds in the Machine: A Brief Review of Online Persuasion Techniques
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Corpus-level behavioral intensity in human–AI conversations
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“Evidence on personalized persuasion indicates that these models can adapt messages to individual profiles and modify beliefs with high communicative effectiveness (Matz et al., 2024; Teeny and Matz, 2024).”
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Metadata
- Authors
- Matz, S. C., Teeny, J. D., Vaid, S. S., Peters, H., Harari, G. M., & Cerf, M.
- Affiliations
- Columbia University (US)
- Northwestern University (US)
- Harvard University (US)
- Stanford University (US)
- Corresponding author
- Matz, S. C.
- Venue
- Scientific Reports
- Published
- 2024-02-26
- Volume
- 14
- Issue
- 1
- Pages
- 4692-4692
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC10897294
- OpenAlex
- W4392153984
- Cited by
- 315
- References
- 74
- Citations by year
- 2026: 133 · 2025: 120 · 2024: 59
- FWCI
- 222.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- Topics
- Social Sciences › Psychology › Social Psychology › Psychology of Social Influence
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- personalized persuasion, generative AI, large language models, ChatGPT, message tailoring, psychological profiling, persuasiveness, political appeal, climate action, political ideology, moral foundations, personality traits
- MeSH terms
- Climate, Language, Morals, Persuasive Communication, Marketing
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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