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Developing Personalized Multi-Role Educational Robot Learning Assistants Based on Large Language Models
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AI
DOI：10.1002/cae.70192.png)
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Abstract
En 中文
Human-robot interaction (HRI) learning plays a crucial role in enhancing the interactivity and enjoyment of the learning process and cultivating future talents with innovative thinking abilities. However, personalized multi-role HRIs in educational contexts remain insufficiently explored. To fill this gap, this study developed a personalized multi-role educational robot learning assistant (MREduRobot), based on the NAO robot and powered by the ERNIE-4.0-Turbo-128K large language model (LLM). The assistant provides personalized video explanations and interactive Q&A and integrates NAO-based voice synthesis to simulate customized teacher roles with variations in gender, personality traits, and teaching style. In addition, a textual semantics matching method was employed to generate context-appropriate actions for the NAO robot in situated learning scenarios. Experimental results showed that the system achieved an average response time of 20.70 s during Q&A interactions (SD = 4.60 s). The questionnaire indicated that learners exhibited a preference for teacher roles centered on logic and innovation. Furthermore, the MREduRobot group significantly outperformed the video group in enhancing the learning experience (learning motivation: Sig(2-tailed) < 0.001; teacher-student interaction: Sig(2-tailed) < 0.001), reducing cognitive load (Sig(2-tailed) < 0.001), and improving learning effectiveness (retention test: Sig(2-tailed) < 0.001; transfer test: Sig(2-tailed) < 0.001). These findings demonstrate the feasibility of using MREduRobot to promote contextual learning through effective HRI.
Keywords:
human-robot interaction
large language models
learning assistants
multi-role educational robot
Journal
C
IF:
2.2
Papers:
124
Citations:
2.3K