Papers
9
Total Citations
198
H-Index
6
About
Yun-Fang Tu is a prominent educational technology researcher whose work sits at the intersection of artificial intelligence, robotics, and early childhood and K-12 learning. Best known for her systematic investigations into AI-based robots in education, Tu has made significant contributions to understanding how robotic systems can be meaningfully integrated into school settings—from preschool classrooms to university-level instruction. Her most-cited work, a 2022 systematic review of AI robots in education (80 citations), established a foundational framework for the field, while her complementary bibliometric analysis of robots in science education (23 citations) traced over two decades of scholarly development in this domain. A distinctive thread running through Tu's research is her commitment to young learners: she has designed and evaluated embodied learning approaches, metaphor-based programming strategies, and incremental project-based methods to make abstract computational concepts accessible to children. Her comparative work on unplugged versus robot programming activities (18 citations) directly informs preschool curriculum design. Beyond programming, Tu has explored social-emotional competence, executive function, and self-regulated learning within robot-assisted environments, reflecting a holistic view of child development. With over 190 cumulative citations, her scholarship offers researchers and educators a rich, evidence-based roadmap for harnessing robotics to transform learning experiences across age groups.
Research Focus
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Top Papers
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