Effects of Embodiment and Personality in LLM-Based Conversational Agents

This work investigates the effects of personality expression and embodiment in conversational agents. We extend a personality-driven conversational agent framework by integrating LLM-based conversation support to provide information about contemporary scientific topics. We describe a user study built on this system to evaluate two opposing personality styles using three models: a dialogue-only model that conveys personality verbally, an animated human model that expresses personality only through dialogue, and an animated human model expressing personality through dialogue and expressive animations. The users perceive all models positively regarding personality and learning outcomes; however, models with high personality traits are perceived as more engaging than those with low personality traits. We provide an analysis of personality perception, learning, and user experience.