Enhancing curriculum innovation in higher education through knowledge graphs: A parallel mediation model of discipline integration, personalized learning pathways, and student engagement.

The present study examines how curriculum innovation in higher education can be better understood in relation to the use of knowledge graphs by employing a parallel mediation model involving discipline integration, personalized learning pathways, and student engagement. Knowledge graphs were conceptualized as a higher-order construct comprising four reflectively measured dimensions: graph architecture, meaning and context, cross-disciplinary capability, and learning pathway support. Data were collected from 413 university students in China through a structured questionnaire, and the proposed model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with bootstrapping procedures. The measurement model demonstrated satisfactory indicator reliability, internal consistency, and validity, with outer loadings exceeding 0.70 for most indicators, composite reliability values ranging from 0.817 to 0.881, and average variance extracted values above 0.50. Discriminant validity was confirmed through the HTMT criterion. The structural model results revealed that knowledge graphs were significantly associated with curriculum innovation (β = 0.595, p < 0.001) and showed indirect relationships through discipline integration (β = 0.236, p < 0.001), personalized learning pathways (β = 0.052, p = 0.007), and student engagement (β = 0.372, p < 0.001). These findings highlight the role of knowledge graphs as a strategic tool for integrating disciplines, personalizing learning experiences, and fostering student engagement, thereby supporting innovative curriculum development in higher education contexts.