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AI-Personalized Persuasion

What it is

Using a language model to compose or conduct persuasion adapted to an individual: their stated reasons, their demographics, or the live course of a conversation.

How it works

Two questions are usually blurred. First, are model-written messages persuasive? Yes, Bai and colleagues (2025) found LLM-written messages shifted attitudes on polarized policies. Second, does personalization add much? Here evidence is mixed. Hackenburg and Margetts (2024, PNAS) found GPT-4 messages microtargeted on demographics and politics were no more persuasive than generic ones. Salvi and colleagues (2025) found that in live debates GPT-4 given basic sociodemographic data had about 81 percent higher odds of winning agreement than human opponents. A December 2025 Science study by Hackenburg and colleagues (nearly 77,000 participants, 19 models) found that post-training and prompting for information density mattered far more than personalization or model size, and that the most persuasive configurations were also less accurate. The same capacity can correct: Costello, Pennycook and Rand (2024) reported roughly 20 percent reductions in conspiracy belief lasting two months, though Science attached an editorial expression of concern about the dataset in June 2026.

Real-world examples

•Costello, Pennycook and Rand had 2,190 conspiracy believers discuss their own stated evidence with GPT-4 Turbo; belief fell by about a fifth on average. In June 2026 Science issued an expression of concern over dataset and screening inconsistencies; the authors say a corrected pipeline reproduces the results, and the journal's evaluation was pending at last review.
•Between November 2024 and March 2025 University of Zurich researchers ran undisclosed AI accounts on Reddit's r/changemyview, posting more than 1,500 comments tailored to users' inferred age, gender, and politics, with some bots claiming to be a rape survivor or a trauma counselor. After moderators disclosed it in April 2025, Reddit called it improper and highly unethical and the researchers said they would not publish.
•In the Hackenburg and Margetts PNAS experiment, a web application fed each participant's demographic and political data into GPT-4 in real time; the tailored messages persuaded, but not measurably more than the untailored ones.
•Bai and colleagues ran three preregistered experiments with 4,829 Americans and found that LLM-written messages moved attitudes on an assault weapons ban, a carbon tax, and paid parental leave, showing the effect is not confined to one set of issues.

Ethical guidelines

Where the line is

Tailoring an accurate, disclosed message to a person's stated concerns is ordinary good communication, and an AI tutor or debunking tool does exactly that. The line is crossed when the AI origin or the sponsor is hidden, when the tailoring relies on inferred sensitive traits the person never offered, when a bot claims a human identity or lived experience it does not have, or when persuasiveness is bought with inaccurate claims.

●Disclose that a person is talking to, or reading text written by, an AI whenever that fact would matter to them.
●Persuade with accurate information. The 2025 Science findings show the pressure runs the other way: optimizing for persuasion degraded accuracy.
●Do not infer and exploit sensitive traits (health, sexuality, trauma, ethnicity) to tune a message.
●Research on persuasion needs consent or a defensible waiver; the Zurich case is now the standard cautionary example.

How to defend against it

►Notice when an argument fits you unusually well. A message that mirrors your identity, your exact objections, and your vocabulary deserves the question: who knows this about me, and how?
►In a persuasive exchange with a chatbot or an unknown account, ask for sources and check two of them yourself. Information-dense replies are the main persuasive lever, and the density can include errors.
►Decide before you look: write down what evidence would change your mind, then see whether the argument supplied it (pre-commitment).
►Name the tactic (Persuasion Knowledge Model, Friestad & Wright 1994). Recognizing that a message was built to move you restores deliberate evaluation.
►Keep scale in mind. Measured effects are a few percentage points per exposure, similar to good human persuasion. The realistic risk is cheap volume and undisclosed sources, not mind control.

References

Hackenburg, K., & Margetts, H. (2024). Evaluating the persuasive influence of political microtargeting with large language models. Proceedings of the National Academy of Sciences, 121(24), e2403116121 · link
GPT-4 messages were persuasive, but microtargeted messages were not statistically more persuasive than generic ones.
Salvi, F., Horta Ribeiro, M., Gallotti, R., & West, R. (2025). On the conversational persuasiveness of GPT-4. Nature Human Behaviour, 9(8), 1645-1653 · link
GPT-4 with sociodemographic data had 81.2 percent higher odds of post-debate agreement than human opponents.
Hackenburg, K., et al. (2025). The levers of political persuasion with conversational artificial intelligence. Science, December 2025 · link
Post-training and prompting raised persuasiveness more than personalization or scale, and reduced factual accuracy.
Costello, T. H., Pennycook, G., & Rand, D. G. (2024). Durably reducing conspiracy beliefs through dialogues with AI. Science, 385(6714), eadq1814; subject of an Editorial Expression of Concern, June 2026 · link
Roughly 20 percent reduction in conspiracy belief persisting two months among 2,190 participants; note the pending expression of concern.
Bai, H., Voelkel, J. G., Muldowney, S., Eichstaedt, J. C., & Willer, R. (2025). LLM-generated messages can persuade humans on policy issues. Nature Communications, 16, 6037 · link
Three preregistered experiments (N = 4,829) showing LLM-written messages shift policy attitudes.

Last reviewed 2026-09-21

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