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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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“While this function allows for more personalised and engaging consent interactions, it may (inadvert-ently) influence participants’ decision-making processes, often without individuals’ specific awareness (Matz et al., 2024; Ye et al., 2024a).”
Warning people that they are being microtargeted fails to eliminate persuasive advantage
Carrella, F., Simchon, A., Edwards, M., & Lewandowsky, S. (2025) · Communications Psychology · doi:10.1038/s44271-025-00188-8 · Cited by 17
“Similarly, targeted messagescrafted by ChatGPT consistently outperformed non-targeted messages across several studies,irrespectiveofthepersuasiondomainandevenwhenprovidedwith onlyasingleshortprompt 17,24 .”
Biswas, S., Erlei, A., & Gadiraju, U. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713201 · Cited by 17
The psychological mechanism of value co-creation with human-centred generative AI robot assistants
Li, Z., Yuen, K. F., & Teo, C. (2025) · Technology in Society · doi:10.1016/j.techsoc.2025.103015 · Cited by 11
Srba, I., Razuvayevskaya, O., Leite, J. A., Moro, R., Schlicht, I. B., Tonelli, S., García, F. M., Lottmann, S. B., Teyssou, D., Porcellini, V., et al. (2026) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3770077 · Cited by 19
Blömker, J., & Albrecht, C. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100126 · Cited by 14
Lim, R. E., Kim, S., Abernathy, A. M., Shin, S., & Avance, R. (2025) · Journal of Marketing Communications · doi:10.1080/13527266.2025.2497259 · Cited by 11
How AI sources can increase openness to opposing views
Lu, L., Tormala, Z. L., & Duhachek, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-00791-z · Cited by 20
“This research also advances an exploding literature on the role of AI in persuasion 52,53 .”
Palm, S., & Tims, M. (2025) · Personality and Individual Differences · doi:10.1016/j.paid.2025.113412 · Cited by 9
Assessing public support for degrowth: survey-based experimental and predictive studies
Krpan, D., Bassó, F., Hickel, J., & Kallis, G. (2025) · The Lancet Planetary Health · doi:10.1016/j.lanplh.2025.101326 · Cited by 12
“The regression models (figure 4) explained 45 ⋅ 31% of the variance in the UK and 51 ⋅ 88% in the USA; comprehensive analysis output is available in the appendix (pp 52–55).”
Neuroscientific Analysis of Logo Design: Implications for Luxury Brand Marketing
Šola, H. M., Khawaja, S., & Qureshi, F. H. (2025) · Behavioral Sciences · doi:10.3390/bs15040502 · Cited by 12
“This approach not only enhances customer satisfaction but also drives business model innovation in the luxury sector (Alipour et al., 2024; Hermann & Puntoni, 2024; Madanchian, 2024; Matz et al., 2024).”
Exploring the Productivity of Generative AI-Powered Ad Campaigns: A Consumer Response Perspective
Yang, J. (2025) · Journal of Interactive Advertising · doi:10.1080/15252019.2025.2551598 · Cited by 12
“Matz et al. (2024) demonstrates the potential for large language to automate and scale the use of personalized persuasion in ways that enhance its effectiveness and efficiency.”
Applications of generative artificial intelligence to influence climate change decisions
Richards, D. R., & Worden, D. (2024) · npj Climate Action · doi:10.1038/s44168-024-00202-5 · Cited by 7
Ethical Foundations of AI-Driven Avatars in the Metaverse for Innovation and User Privacy
Almomani, A., Al–Qerem, A., Alauthman, M., Aldweesh, A. Y., Aoudi, S., & Salloum, S. A. (2025) · IEEE Access · doi:10.1109/access.2025.3589714 · Cited by 11
“According to recent market analyses, the Metaverse economy could surpass hundreds of billions of dollars in value within the coming decade [7].”
Rodríguez-Sánchez, C., Esper, F. S., Casaló, L. V., & López, M. (2025) · Technology in Society · doi:10.1016/j.techsoc.2025.103036 · Cited by 7
Using artificial intelligence in digital branding: a two-phase qualitative approach
Jalilvand, M. R., & Ataei, J. (2026) · EuroMed Journal of Business · doi:10.1108/emjb-12-2024-0353 · Cited by 7
Zhang, Y., Huang, B., Yuan, W., Jiang, Z., Peng, L., Chen, S., & Tan‐Soo, J. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104090 · Cited by 7
How the Role of Generative AI Shapes Perceptions of Value in Human-AI Collaborative Work
Schecter, A., & Richardson, B. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713946 · Cited by 15
“Subsequently, AI can refine this content, focusing on optimizing accuracy, coherence, and stylistic consistency by leveraging its unique strengths in stability and consistency [25], memory [22], personalization [27], [28], [29], iterability [30], [31], and speed and efficiency [3].”
Trzebiński, W., Konuk, F. A., & Marciniak, B. (2025) · Journal of Business Research · doi:10.1016/j.jbusres.2025.115487 · Cited by 7
Twenty Years of Personality Computing: Threats, Challenges and Future Directions
Celli, F., Kartelj, A., Dordevič, M., Suhartono, D., Filipović, V., Milutinović, V. M., Spathoulas, G. P., Vinciarelli, A., Kosiński, M., & Lepri, B. (2026) · ACM Computing Surveys · doi:10.1145/3806009 · Cited by 16
“Additional Key Words and Phrases: Personality Computing, Personality Recognition, Personality Perception, Personality Synthesis”
Is Undisclosed LLM Use Morally Wrong?
Sahebi, S., & Montefiore, T. (2026) · Philosophy & Technology · doi:10.1007/s13347-026-01094-5 · Cited by 6
“…tailor personalised messages based on psychological profiles or personality types leads to significantly greater persua - sion compared to non-personalised messages in a variety of contexts, including political messaging or marketing of consumer products (Matz et al., 2024; Simchon et al., 2024).”
La inteligencia artificial en la publicidad: una revisión sistemática de la década 2020-2024
Sanguineti, R. G. B., Córdova, C. D. C., & Baltazar, A. T. (2024) · Journal of the Academy · doi:10.47058/joa11.4 · Cited by 6
Epstein, R., Newland, A., & Tang, L. (2025) · PLoS ONE · doi:10.1371/journal.pone.0322900 · Cited by 6
“AI-generated political content has already been shown to be highly persuasive, especially when that content is also personalized [116,117].”
Can generative artificial intelligence help or hinder sustainable marketing?
McCarthy, B. (2024) · Journal of Resilient Economies (ISSN 2653-1917) · doi:10.25120/jre.4.2.2024.4153 · Cited by 2
Artificial intelligence, digital social networks, and climate emotions
Galaz, V., Metzler, H., Schill, C., Lindahl, T., Daume, S., Marklund, A., Castro, A. A., Bard, J. S., McPhearson, T., Galafassi, D., et al. (2025) · npj Climate Action · doi:10.1038/s44168-025-00225-6 · Cited by 6
“Intheformercase,studiessuggestthatGenAIcanbeused tonotonlyproducehighlyrealisticsyntheticcontent(i.e.,text,images, videos),butalsopromptsuchproductiontospeci fi callytargettheemotional state, age, and/or ideological predisposition of a target audience 68 – 70 .”
Self-citationMotivation profiles: understanding interplay of persuasive strategies and self-determination theory
Orji, F. A., Gutiérrez, F. J., & Vassileva, J. (2025) · Behaviour and Information Technology · doi:10.1080/0144929x.2025.2522200 · Cited by 10
“Research demonstrates that aligning persuasive messages with users’ personality profiles – such as tailoring messages to appeal to extro-verts versus introverts – significantly increased the effectiveness of persuasion (Hirsh, Kang, and Bodenhausen 2012; Matz et al. 2024).”
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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