Feasibility and Pathways of Using AI Knowledge Graphs to Innovate Postgraduate English Terminology Teaching in Literary Theory

Abstract

The teaching of English terminology in literary theory to graduate students often struggles with conceptual fragmentation, poorly established connections between terms, and ineffective application in academic practice. To tackle these issues, this paper first diagnoses the specific pedagogical challenges through a systematic review of relevant literature and semi-structured interviews with teachers and students. Building on this diagnosis, it then proposes and theoretically justifies the integration of AI knowledge graphs as a solution. The justification is structured around three core aspects: how knowledge graphs can enhance structured knowledge representation, align with graduate students' cognitive patterns for understanding abstract concepts, and be embedded into the full spectrum of teaching contexts . Findings indicate that AI knowledge graphs can effectively mitigate terminology fragmentation via structured representation, support the comprehension of abstract concepts through visual relational mapping, and enable dynamic, personalized engagement across all phases of learning. By establishing this three-pronged feasibility and a Learning-Research-Application dynamic teaching model for terminology literacy, the research contributes a foundational theoretical rationale and a viable implementation pathway for harnessing AI knowledge graphs in humanities terminology education. The proposed approach serves as a transferable model for enhancing discipline-specific English pedagogy across related fields.