Abstract
This study investigates the integration of individual human traits into an empathetically adaptive educational robot tutor system designed to improve student engagement and learning outcomes with corresponding Engagement Vector measurement. While prior research in the field of Human-Robot Interaction (HRI) has examined the integration of the traits, such as emotional intelligence, memory-driven personalization, and non-verbal communication, by themselves, they have thus-far neglected to consider their synchronized integration into a cohesive, operational education framework. To address this gap, we customize a Multi-Modal Large Language Model (LLaMa 3.2 from Meta) deployed with modules for human-like traits (emotion, memory and gestures) into an AI-Agent framework. This constitutes to the robot's intelligent core mimicing the human emotional system, memory architecture and gesture control to allow the robot to behave more empathetically while recognizing and responding appropriately to the student's emotional state. It can also recall the student's past learning record and adapt its style of interaction accordingly. This allows the robot tutor to react to the student in a more sympathetic manner by delivering personalized verbal feedback synchronized with relevant gestures. Our study investigates the extent of this effect through the introduction of Engagement Vector Model which can be a surveyor's pole for judging the quality of HRI experience. Quantitative and qualitative results demonstrate that such an empathetic responsive approach significantly improves student engagement and learning outcomes compared with a baseline humanoid robot without these human-like traits. This indicates that robot tutors with empathetic capabilities can create a more supportive, interactive learning experience that ultimately leads to better outcomes for the student.
Abstract (translated)
这项研究调查了将个体人类特质整合到一种旨在通过相应的参与向量测量提高学生参与度和学习成果的同理心适应性教育机器人辅导系统中的情况。尽管先前关于人机交互（HRI）领域的研究已经分别考察了情感智能、基于记忆的个性化以及非言语交流等特质，但它们尚未考虑将这些特质同步整合到一个连贯的操作教育框架中。为了解决这一空白，我们定制了一个多模态大型语言模型（Meta开发的LLaMa 3.2），并部署具有人类特质模块（情绪、记忆和手势）的人工智能代理框架。这构成了机器人智能核心的基础，模仿了人的情感系统、记忆架构以及手势控制，使机器人能够以更富有同理心的方式行为，并且能够识别和适当响应学生的情绪状态。此外，该机器人还可以回忆学生的过往学习记录并相应地调整互动方式。这一过程使得辅导机器人可以更加同情地回应学生，通过个性化的言语反馈与相关的手势同步进行互动。 我们的研究通过引入参与向量模型来探讨这种效应的程度，这可以作为一种衡量HRI体验质量的指标。定量和定性结果表明，这种富有同理心的响应方式显著提高了学生的参与度和学习成果，相较于没有这些人类特质的人形机器人基线而言效果更为出色。这意味着具备同情能力的辅导机器人能够创造一个更加支持性和互动性的学习环境，从而最终为学生带来更好的成绩。
URL
https://arxiv.org/abs/2505.19803
