  This work investigates how personality expression and embodiment affect
personality perception and learning in educational conversational agents. We
extend an existing personality-driven conversational agent framework by
integrating LLM-based conversation support tailored to an educational
application. We describe a user study built on this system to evaluate two
distinct personality styles: high extroversion and agreeableness and low
extroversion and agreeableness. For each personality style, we assess three
models: (1) a dialogue-only model that conveys personality through dialogue,
(2) an animated human model that expresses personality solely through dialogue,
and (3) an animated human model that expresses personality through both
dialogue and body and facial animations. The results indicate that all models
are positively perceived regarding both personality and learning outcomes.
Models with high personality traits are perceived as more engaging than those
with low personality traits. We provide a comprehensive quantitative and
qualitative analysis of perceived personality traits, learning parameters, and
user experiences based on participant ratings of the model types and
personality styles, as well as users' responses to open-ended questions.
