Qingtang Liu
Papers
2
Total Citations
10
H-Index
2
About
Qingtang Liu is a leading researcher at the intersection of artificial intelligence and education, with a primary focus on knowledge graph technology and its intelligent applications in learning environments. His work explores how semantic networks can revolutionize knowledge representation, search reasoning, and educational resource management. In his highly cited 2022 review, Liu provides a comprehensive analysis of educational knowledge graphs from an AI perspective, establishing foundational frameworks that have garnered significant attention in the field. He has also made notable contributions to understanding feedback mechanisms in online learning, particularly during the COVID-19 pandemic. His research on micro-video course learning examines how different feedback strategies impact student outcomes in blended learning contexts, addressing critical challenges in digital pedagogy. With citations reaching into the dozens for his most influential work, Liu’s scholarship is shaping how educators and technologists design intelligent, adaptive learning systems. His recent achievements include advancing the theoretical and practical integration of AI-driven knowledge graphs into mainstream education, positioning him as a key voice in the future of smart learning technologies.
Research Focus
Key Achievements
Top Papers
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