Generative social agents (GSAs) use artificial intelligence to autonomously communicate with human users in a natural and adaptive manner. Currently, there is a lack of theorizing regarding interactions with GSAs, and likewise, few guidelines exist for studying how they influence user attitudes and behaviors. Consequently, we propose the Knowledge-based Persuasion Model (KPM) as a novel theoretical framework. According to the KPM, a GSA's self, user, and context-related knowledge drives its persuasive behavior, which in turn shapes the attitudes and behaviors of a responding human user. By synthesizing existing research, the model offers a structured approach to studying interactions with GSAs, supporting the development of agents that motivate rather than manipulate humans. Accordingly, the KPM encourages the integration of responsible GSAs that adhere to social norms and ethical standards with the goal of increasing user wellbeing. Implications of the KPM for research and application domains such as healthcare and education are discussed.

翻译：生成式社交代理（GSAs）利用人工智能，以自然且自适应的方式与人类用户进行自主交流。目前，关于与GSAs交互的理论研究尚显不足，同样，也缺乏研究其如何影响用户态度和行为的指导原则。为此，我们提出了基于知识的说服模型（KPM）作为一种新颖的理论框架。根据KPM，GSA关于自身、用户及情境的知识驱动其说服行为，进而塑造回应的人类用户的态度与行为。通过整合现有研究，该模型为研究GSA的交互提供了一种结构化方法，支持开发旨在激励而非操纵人类的代理。因此，KPM鼓励整合遵循社会规范和伦理标准、以提升用户福祉为目标的负责任GSAs。本文还讨论了KPM在医疗保健和教育等研究与应用领域中的意义。