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Leveraging Knowledge Graphs and AI to Empower Personalized Learning in English Literature Course
ZHANG Ruoxi, LI Hao, HE Guangshuo, WANG Zeyu, WU Dingyutong, ZHANG Wenwen
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DOI:10.17265/2161-623X/2026.08.007
Ningbo University, Ningbo, China
Against the backdrop of globalization and global education digitization, traditional English literature instruction for English majors in higher education faces the dual challenges of fragmented knowledge and homogenized learning pathways. Grounded in constructivism and adaptive learning theory, this study leverages a smart teaching platform to construct an English literature knowledge graph model encompassing four major genres—novels, poetry, drama, and prose—thereby establishing semantic connections among multidimensional entities such as authors, works, and themes. Empirical research (N = 40) indicates that this knowledge graph not only significantly enhances students’ cognitive completeness regarding the literary genre system with an overall student satisfaction rate reaching 92.5%, but also effectively stimulates their independent learning and critical thinking. Finally, this paper discusses the challenges regarding the graph’s semantic recognition accuracy and dynamic update mechanisms, and proposes corresponding strategies for optimizing the integration of technology and teaching.
knowledge graph, English literature, personalized learning, ChaoXing platform, digital education
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