Personalized to Persuade: The Effects of Contextualization and Warmth on Trust and Reliance in Conversational AI
arXiv:2605.31275v1 Announce Type: cross Abstract: Artificial Intelligence (AI) agents personalize their responses by tailoring explanations to users' backgrounds, interests, and prior interactions, referred to as contextualization. Personalization has been identified as a persuasive strategy in politics or in marketing. However, the persuasive effect of contextualization in everyday tasks, where users often lack prior knowledge, remains unclear. We conducted a $2\times2$ between-subjects experiment ($N = 380$) examining how contextualization, combined with conversational warmth, shapes relianc
The proliferation of conversational AI across various applications means understanding its persuasive mechanisms is critical for development and regulation.
This research provides insights into how AI agents can build trust and influence user behavior through contextualization and conversational warmth, impacting user adoption and ethical AI design.
Our understanding of AI's persuasive capabilities shifts from basic interaction to nuanced psychological manipulation, potentially leading to more effective, or more concerning, AI agent design.
- · AI developers
- · Marketing and sales professionals
- · Companies deploying AI agents
- · Naive AI users
- · Ethical AI advocates (if unchecked)
AI agents become more adept at persuasive communication, increasing user engagement and task completion rates.
The enhanced persuasive abilities of AI could lead to new forms of marketing, sales, and even social engineering.
As AI's influence grows, regulatory bodies may introduce new guidelines for transparency and ethical persuasion in AI interactions.
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Read at arXiv cs.AI