ORIGINAL RESEARCH article
Front. Robot. AI
Sec. Computational Intelligence in Robotics
Volume 12 - 2025 | doi: 10.3389/frobt.2025.1635419
This article is part of the Research TopicSynergizing Large Language Models and Computational Intelligence for Advanced Robotic SystemsView all 5 articles
Integrating emotional intelligence, memory architecture, and gestures to achieve empathetic humanoid robot interaction in an educational setting
Provisionally accepted
- 1University of Liverpool, Liverpool, United Kingdom
- 2Xi'an Jiaotong-Liverpool University, Suzhou, China
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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 measurements. 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 the robot's intelligent core that mimics the human emotional system, memory architecture and gesture controller 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 suggests the extent of this effect through the introduction of Engagement Vector Model which can be a benchmark 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.
Keywords: Empathetic Educational Robots, Human-robot interaction (HRI), Multimodal Personalization, Behavioral Analytics in Learning, student engagement
Received: 26 May 2025; Accepted: 08 Aug 2025.
Copyright: © 2025 Sun, Li, Meng, Teng, Payne and Craig. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Fuze Sun, University of Liverpool, Liverpool, United Kingdom
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