The Promise of Generative AI for Personalized Persuasion

Generative AI (GenAI) has transformed many aspects of modern life. In this article, we explore the potential of GenAI for personalized persuasion: the process of tailoring the content of a persuasive message to an individual’s unique preferences, needs, and motivations. First, we describe how GenAI addresses the “identification-customization” challenge in personalized persuasion, and we offer a scoping review of the research supporting its effectiveness to date. Second, we introduce a framework that allows researchers to classify a study’s empirical approach according to several critical design choices, including (a) the data sources and types of personal information used to determine the personalized elements, (b) the complexity of the personalization approach, and (c) the delivery of the personalized content. This framework facilitates the integration of existing work and identifies important gaps in our current knowledge. We discuss promising avenues for future research throughout and conclude with a discussion of important ethical considerations.

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Publication Details

Journal
Current Directions in Psychological Science
Published
2026-05-19
DOI
https://doi.org/10.1177/09637214261424859
Primary Topic
Innovative Human-Technology Interaction
Type
article
Field-Weighted Citation Impact
0.00
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article

The Promise of Generative AI for Personalized Persuasion

Johannes Luther, Jacob D. Teeny, Sandra Matz, Kimberly Klugescheid
Current Directions in Psychological Science
Innovative Human-Technology Interaction
article

The Promise of Generative AI for Personalized Persuasion

Johannes Luther, Jacob D. Teeny, Sandra Matz, Kimberly Klugescheid
article en

Abstract

Generative AI (GenAI) has transformed many aspects of modern life. In this article, we explore the potential of GenAI for personalized persuasion: the process of tailoring the content of a persuasive message to an individual’s unique preferences, needs, and motivations. First, we describe how GenAI addresses the “identification-customization” challenge in personalized persuasion, and we offer a scoping review of the research supporting its effectiveness to date. Second, we introduce a framework that allows researchers to classify a study’s empirical approach according to several critical design choices, including (a) the data sources and types of personal information used to determine the personalized elements, (b) the complexity of the personalization approach, and (c) the delivery of the personalized content. This framework facilitates the integration of existing work and identifies important gaps in our current knowledge. We discuss promising avenues for future research throughout and conclude with a discussion of important ethical considerations.

Current Directions in Psychological Science
Kellogg's (Canada) (CA), University of Göttingen (DE), Columbia University (US), Ludwig-Maximilians-Universität München (DE)
Openalex Percentile: Top 3%
Innovative Human-Technology Interaction
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