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<p>Educational robots exhibit considerable potential for adaptive learning. However, current implementations often limit their role to knowledge transmission through teacher-centred approaches, potentially constraining pre-service teachers’ development of pedagogical skills. This study proposes and evaluates a knowledge graph-assisted educational robot (KGAER) system designed to scaffold structured domain knowledge and support self-regulated learning. Using a quasi-experimental design, pre-service teachers were assigned to an experimental group (<i>n</i> = 39) that learned with KGAER, or a control group (<i>n</i> = 46) that used a conventional educational robot. Results indicated that the KGAER group demonstrated significantly higher instructional design quality (<i>F</i> = 11.91, <i>p</i> &lt; 0.01, <i>η²</i> = 0.13), greater flow experience (<i>t</i> = 3.07, <i>p</i> &lt; 0.05, <i>d</i> = 0.67), and stronger overall motivation (<i>F</i> = 8.02, <i>p</i> &lt; 0.05) compared with the control group. Cognitive load did not differ significantly between groups, suggesting that the intervention did not impose an additional mental burden. These findings support the use of knowledge graphs to enhance the instructional effectiveness of educational robots, illustrating how human–AI collaboration can maintain pedagogical agency while improving learning outcomes in teacher education.</p>

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Effects of knowledge graph assisted educational robots on pre-service teachers’ instructional design quality, flow experience, motivation, and cognitive load

  • 期刊论文

  • 发表日期：

    2026年7月1日

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  • Liu-Jie Xu,
  • Shi-Jin Chen,
  • Long-Fei Su,
  • Gwo-Jen Hwang,
  • Hui-Hui Wu,
  • He-Hai Liu,
  • Wen-Tao Wu

摘要

Educational robots exhibit considerable potential for adaptive learning. However, current implementations often limit their role to knowledge transmission through teacher-centred approaches, potentially constraining pre-service teachers’ development of pedagogical skills. This study proposes and evaluates a knowledge graph-assisted educational robot (KGAER) system designed to scaffold structured domain knowledge and support self-regulated learning. Using a quasi-experimental design, pre-service teachers were assigned to an experimental group (n = 39) that learned with KGAER, or a control group (n = 46) that used a conventional educational robot. Results indicated that the KGAER group demonstrated significantly higher instructional design quality (F = 11.91, p < 0.01, η² = 0.13), greater flow experience (t = 3.07, p < 0.05, d = 0.67), and stronger overall motivation (F = 8.02, p < 0.05) compared with the control group. Cognitive load did not differ significantly between groups, suggesting that the intervention did not impose an additional mental burden. These findings support the use of knowledge graphs to enhance the instructional effectiveness of educational robots, illustrating how human–AI collaboration can maintain pedagogical agency while improving learning outcomes in teacher education.