Exploring the Potential and Limitations of Large Language Models to Control the Behavior of Embodied Persuasive Agents (BEST PAPER AWARD)
Authors:Corrò C., Chittaro L.
Published in:Proceedings of PERSUASIVE 2025: 20th International Conference on Persuasive Technologies, LNCS 15711, Springer Nature Switzerland AG, Cham, May 2025.
Abstract:Interactive agents are an essential element of many persuasive applications. Their design and development have so far required extensive human effort to model their appearance and behavior. However, recent advances in the generative capabilities of Large Language Models (LLMs) might pave the way to build persuasive agents capable of autonomous, open-ended interactions without requiring the traditional investment in agent development. In this paper, we investigate the creation of an LLM-based embodied agent aimed at interacting with users in real-time to coach them in performing slow and deep breathing. In the approach we followed, the LLM uses a text-based context to generate a composition of predefined behaviors for interacting with the user through both verbal and nonverbal communication. The text-based context provided to the LLM described essential details, like the user’s respiratory rate, to monitor the exercise. Information about actual user’s breathing was provided to the LLMmodel through a physiological sensor. The LLM-based breathing coach managed to follow the exercise structure and generated believable contingent behavior compositions. However, as we describe in the paper, building and evaluating the system allowed to highlight limitations of using only LLMs to create agents capable of real-time user interactions. The identified limitations suggest a need for hybrid approaches.
