Never say never: Exploring the effects of available knowledge on agent persuasiveness in controlled physiotherapy motivation dialogues

Generative Social Agents (GSAs) are increasingly impacting human users through persuasive means. On the one hand, they might motivate users to pursue personal goals, such as healthier lifestyles. On the other hand, they are associated with potential risks like manipulation and deception, which are induced by limited control over probabilistic agent outputs. However, as GSAs manifest communicative patterns based on available knowledge, their behavior may be regulated through their access to such knowledge. Following this approach, we explored persuasive ChatGPT-generated messages in the context of human-robot physiotherapy motivation. We did so by comparing ChatGPT-generated responses to predefined inputs from a hypothetical physiotherapy patient. In Study 1, we qualitatively analyzed 13 ChatGPT-generated dialogue scripts with varying knowledge configurations regarding persuasive message characteristics. In Study 2, third-party observers (N = 27) rated a selection of these dialogues in terms of the agent's expressiveness, assertiveness, and persuasiveness. Our findings indicate that LLM-based GSAs can adapt assertive and expressive personality traits -- significantly enhancing perceived persuasiveness. Moreover, persuasiveness significantly benefited from the availability of information about the patients' age and past profession, mediated by perceived assertiveness and expressiveness. Contextual knowledge about physiotherapy benefits did not significantly impact persuasiveness, possibly because the LLM had inherent knowledge about such benefits even without explicit prompting. Overall, the study highlights the importance of empirically studying behavioral patterns of GSAs, specifically in terms of what information generative AI systems require for consistent and responsible communication.

Knowledge-Based Design Requirements for Generative Social Robots in Higher Education

Generative social robots (GSRs) powered by large language models enable adaptive, conversational tutoring but also introduce risks such as hallucina-tions, overreliance, and privacy violations. Existing frameworks for educa-tional technologies and responsible AI primarily define desired behaviors, yet they rarely specify the knowledge prerequisites that enable generative systems to express these behaviors reliably. To address this gap, we adopt a knowledge-based design perspective and investigate what information tutor-ing-oriented GSRs require to function responsibly and effectively in higher education. Based on twelve semi-structured interviews with university stu-dents and lecturers, we identify twelve design requirements across three knowledge types: self-knowledge (assertive, conscientious and friendly per-sonality with customizable role), user-knowledge (personalized information about student learning goals, learning progress, motivation type, emotional state and background), and context-knowledge (learning materials, educa-tional strategies, course-related information, and physical learning environ-ment). By identifying these knowledge requirements, this work provides a structured foundation for the design of tutoring GSRs and future evaluations, aligning generative system capabilities with pedagogical and ethical expecta-tions.

Understanding Persuasive Interactions between Generative Social Agents and Humans: The Knowledge-based Persuasion Model (KPM)

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.