The Advantages and Constraints of LLM-Augmented Knowledge Graphs in University Innovation and Entrepreneurship Education

This paper investigates the integration of Large Language Models with Knowledge Graphs (LLM-KGs) into the pedagogical framework of higher education, specifically within innovation and entrepreneurship programs. It argues that this synergy can catalyze a shift toward more intelligent and data-informed educational practices. However, the implementation is scrutinized through a critical lens, revealing significant constraints in data governance, a growing digital divide among faculty, and systemic institutional inertia. In response, the study proposes a set of strategic interventions designed to provide both a conceptual foundation and actionable guidance for the effective adoption of LLM-KG systems in university settings

Conceptual framework · Conceptual model · Entrepreneurship · Psychological intervention · Advanced Graph Neural Networks · E-Learning and Knowledge Management · Research Data Management Practices