From Barriers to Bridges: A TPACK-XL Framework for Knowledge Graph Integration in Higher Education

Abstract

As a critical infrastructure in higher education digital transformation, educational knowledge graphs play an essential role in promoting artificial intelligence-driven personalized learning. However, university faculty members face the practical dilemma of high digital literacy (82.98% with MOOC/SPOC development experience), low technology adoption (31.91% among n=47)'. Based on the TPACK-XL theoretical framework, this study systematically revealed the multidimensional barriers encountered by faculty members when applying educational knowledge graphs. This investigation employed a mixed-methods approach, encompassing a sample size of 47 participants. The study found that technological complexity (composite score of 5.67), superficial pedagogical integration (dynamic diagnostic score of 2.87), and disciplinary differences (STEM application rate of more than 20%, while humanities fields are almost blank) constitute the main challenges in the pedagogical application of educational knowledge graphs. It essentially stems from the synergistic imbalances among the TPACK-XL framework's core components: Technology (T), Pedagogy (P), Content (C), Learner (L), and Context (X). To bridge these gaps, the study proposes a four-pronged systemic integration framework: the development of a cross-platform sharing mechanism and data processing technical tools to enhance T-X synergy, the establishment of a interdisciplinary collaborative mechanism to achieve the P-X bridge, the designed teaching training to balance C-X, and personalized learning took into consideration to balance L-X. This study contributes to the development of TPACK-XL theory by examining the barriers to the application of artificial intelligence in higher education in specific contexts, and it provides a theoretical framework and a practical path for solving the 'last mile' problem in the application of educational technology.