提出知识驱动的劝服模型，解释生成式社交代理如何影响人类态度与行为。
Understanding Persuasive Interactions between Generative Social Agents and Humans: The Knowledge-based Persuasion Model (KPM)
- 基于自我、用户和情境三类知识构建劝服机制
- 模型可指导开发促进用户福祉而非操控的智能代理
- 适用于医疗、教育等需伦理交互的领域
生成式社交代理（GSAs）利用人工智能自主地以自然且自适应的方式与人类用户互动。当前缺乏对这类交互的理论框架，也少有研究指南来探讨其如何影响用户态度与行为。为此，本文提出知识驱动的劝服模型（KPM），认为代理的自我知识、用户知识及情境知识共同驱动其劝服行为，进而影响人类用户的认知与行为。该模型基于现有研究，提供系统化方法，支持开发旨在激励而非操纵用户的代理。通过整合符合社会规范与伦理标准的负责任代理，有助于提升用户福祉。文中还报告了初步评估结果，并讨论了该模型在医疗、教育等领域的研究与应用启示。
原文摘要 · Abstract (English)
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-knowledge drives its persuasive behavior, which in turn shapes attitudes and behaviors of a responding human user. Building on existing research, the model offers a structured approach to studying interactions with GSAs, supporting the development of agents that motivate rather than manipulate human users. Accordingly, the KPM encourages the integration of responsible GSAs that adhere to social norms and ethical standards with the goal of increasing user wellbeing. A preliminary evaluation study, as well as implications of the KPM for research and application domains such as healthcare and education are discussed.
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