# Exploring persuasive interactions with generative social robots: An experimental framework

arXiv: https://arxiv.org/abs/2509.03231 · HTML: https://mlsanity.com/paper/2509.03231/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2025.

## Built on by (most cited first, 3 of 3)

- [Understanding Persuasive Interactions between Generative Social Agents and Humans: The Knowledge-based Persuasion Model (KPM)](https://mlsanity.com/paper/2602.11483/) (2026)
- [Knowledge-Based Design Requirements for Generative Social Robots in Higher Education](https://mlsanity.com/paper/2602.12873/) (2026)
- [Never say never: Exploring the effects of available knowledge on agent persuasiveness in controlled physiotherapy motivation dialogues](https://mlsanity.com/paper/2602.12924/) (2026)

## Abstract (arXiv)

Integrating generative AI such as Large Language Models into social robots has improved their ability to engage in natural, human-like communication. This study presents a method to examine their persuasive capabilities. We designed an experimental framework focused on decision making and tested it in a pilot that varied robot appearance and self-knowledge. Using qualitative analysis, we evaluated interaction quality, persuasion effectiveness, and the robot's communicative strategies. Participants generally experienced the interaction positively, describing the robot as competent, friendly, and supportive, while noting practical limits such as delayed responses and occasional speech-recognition errors. Persuasiveness was highly context dependent and shaped by robot behavior: Participants responded well to polite, reasoned suggestions and expressive gestures, but emphasized the need for more personalized, context-aware arguments and clearer social roles. These findings suggest that generative social robots can influence user decisions, but their effectiveness depends on communicative nuance and contextual relevance. We propose refinements to the framework to further study persuasive dynamics between robots and human users.