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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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Generative artificial intelligence enhances creativity but reduces the diversity of novel content
Doshi, A., & Hauser, O. (2023) · doi:10.2139/ssrn.4535536 · Cited by 1,350
“That a tightly controlled prompt requesting a generative AI idea shows sizable effects on creativity in our study provides a promising starting point for future researchers to delve deeper into customization and personalization of generative AI for different writers ( 8 ).”
Bengesi, S., El-Sayed, H., Sarker, M. K., Houkpati, Y., Irungu, J., & Oladunni, T. (2024) · IEEE Access · doi:10.1109/access.2024.3397775 · Cited by 470
Durably reducing conspiracy beliefs through dialogues with AI
Costello, T. H., Pennycook, G., & Rand, D. G. (2024) · Science · doi:10.1126/science.adq1814 · Cited by 354
Brüns, J. D., & Meißner, M. (2024) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2024.103790 · Cited by 400
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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
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