Abstract
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.
Abstract (translated)
将生成式人工智能（如大型语言模型）集成到社交机器人中，提高了它们进行自然、人性化的交流的能力。本研究提出了一种方法来考察这些机器人的说服力。我们设计了一个以决策制定为中心的实验框架，并在一个试点项目中测试了这一框架，该试点项目调整了机器人的外观和自我认知情况。通过定性分析，我们评估了交互质量、说服效果以及机器人的沟通策略。 参与者通常对与机器人互动体验感到积极，他们认为机器人既聪明又友好且支持性强，但同时也指出了实际存在的限制，如延迟回应和偶尔出现的语音识别错误。说服力高度依赖于具体情境，并受机器人行为的影响：参与者对于礼貌、有理据的支持以及表达性手势做出了良好反应，但也强调需要更多个性化的、语境相关的论点以及更明确的社会角色定义。 这些发现表明，生成式的社交机器人能够影响用户决策，但其效果取决于沟通技巧的微妙性和与情境的相关性。我们建议对实验框架进行改进，以便进一步研究机器人和人类用户之间说服动态的变化规律。
URL
https://arxiv.org/abs/2509.03231
