测试生成式社交机器人如何通过自然对话影响人类决策。
Exploring persuasive interactions with generative social robots: An experimental framework
- 设计实验框架，考察机器人外观与自我认知对说服力的影响。
- 用户认为机器人友善且可靠，但响应延迟和识别错误影响体验。
- 礼貌理性表达+情感动作更易说服，需更强个性化与情境感知。
将大语言模型等生成式AI融入社交机器人，显著提升了其自然对话能力。本研究提出一种实验框架，聚焦决策过程，通过试点实验考察机器人外观与自我认知差异的影响。采用定性分析评估交互质量、说服效果及沟通策略。参与者普遍反馈积极，认为机器人具备能力、友好且支持性强，但也指出响应延迟和语音识别错误等实际限制。说服效果高度依赖情境，表现为对礼貌、有理据的建议及富有表现力的姿态反应良好，但强调需更个性化、情境感知更强的论证方式以及更清晰的社会角色定位。结果表明，生成式社交机器人可影响用户决策，但其有效性取决于沟通细节与上下文相关性。研究提出对框架的改进方向，以深入探究人机说服动态。
原文摘要 · Abstract (English)
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.
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