具身性与人格表达对基于LLM的教育智能体学习效果的影响
The Effects of Embodiment and Personality Expression on Learning in LLM-based Educational Agents
AI总结：
本研究扩展人格驱动对话框架，集成LLM对话支持，通过用户研究评估高/低外向性与宜人性人格及三种模型（纯对话、动画人物仅对话、动画人物对话加动画）对教育智能体人格感知与学习的影响，发现所有模型均获积极评价，高人格特质模型更具吸引力。
摘要：
本研究探讨了人格表达与具身性如何影响教育型对话智能体中的人格感知与学习效果。我们扩展了一个现有的人格驱动对话智能体框架，通过整合针对教育应用定制的基于LLM的对话支持。我们基于该系统开展了一项用户研究，以评估两种截然不同的人格风格：高外向性与高宜人性，以及低外向性与低宜人性。针对每种人格风格，我们评估了三种模型：（1）仅通过对话传达人格的纯对话模型；（2）仅通过对话表达人格的动画人物模型；（3）通过对话以及身体和面部动画共同表达人格的动画人物模型。结果表明，所有模型在人格感知和学习成果方面均获得积极评价。具有高人格特质的模型被认为比具有低人格特质的模型更具吸引力。我们基于参与者对模型类型和人格风格的评分，以及用户对开放式问题的回答，提供了关于感知人格特质、学习参数和用户体验的全面定量与定性分析。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
