Understanding Persuasive Interactions between Generative Social Agents and Humans: The Knowledge-based Persuasion Model (KPM)
This provides a foundational framework for researchers and developers in AI and human-computer interaction to study and design ethical GSAs, though it is incremental as it synthesizes existing research into a structured model.
The paper tackles the lack of theoretical frameworks for understanding how generative social agents (GSAs) influence human attitudes and behaviors by proposing the Knowledge-based Persuasion Model (KPM), which posits that a GSA's knowledge drives persuasive interactions to shape user responses, aiming to support the development of agents that motivate rather than manipulate humans.
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.