Shenghui Song
Papers
4
Total Citations
65
H-Index
4
About
Shenghui Song is a pioneering researcher at the intersection of educational technology, human-robot interaction, and second-language acquisition. Her work centers on how humanoid and multi-modal robots can transform language learning by addressing students’ diverse motivational and cognitive needs. Song’s major contributions include empirically demonstrating that robot-assisted learning, grounded in self-determination theory, significantly boosts learner autonomy, competence, and relatedness—key drivers of engagement. Her 2024 study on humanoid robot-empowered language learning has already garnered 32 citations, underscoring its timely impact. In a 2025 comparative study (23 citations), she showed that different robot designs yield distinct effects on student engagement, offering crucial design guidelines for inclusive classrooms. Song also tackles real-world challenges, such as teaching Chinese to mixed-background learners—including students with dyslexia and non-Chinese speakers—proving that adaptive robotic tutors can bridge learning gaps. Her work is notable for its rigorous empirical methodology and its focus on equity, making her a leading voice in the push toward personalized, technology-enhanced education.
Research Focus
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Top Papers
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