INNOVATIVE APPROACHES TO EDUCATIONAL KNOWLEDGE GRAPHS: LEVERAGING NEO4J AND LLM FOR SYNTHETIC DATA GENERATION

In todays rapidly evolving higher education landscape, the utilization of data is critical for gaining insights that enhance institutional effectiveness. However, accessing and leveraging this data presents significant challenges due to privacy, licensing, and security constraints. This research evaluates the effectiveness of using Large Language Model (LLM)-generated synthetic data for constructing and visualizing educational knowledge graphs (KGs) using Neo4j. By integrating Neo4j's graph capabilities with LLM's generative power, we developed comprehensive knowledge graphs across five key university domains: curriculum development, personalized learning, learning analytics, resource management, and alumni engagement. Our methodology employs a two-stage prompting strategy with Claude 3.5 Sonnet to generate synthetic datasets that simulate real-world educational scenarios, achieving data completeness rates of 77.55-100% and domain alignment scores of 82.84-100%. The synthetic data successfully captured complex educational relationships through varied node types (5-16) and relationship types (4-16) across domains, while relationship accuracy metrics (38.21-46.55%) demonstrated focused, meaningful connections. Implementation results show efficient processing times (3-8 seconds per domain) and strong practical utility in modelling educational patterns and relationships. This approach significantly reduces data collection time and resources while maintaining data privacy and integrity. While limitations exist regarding validation against real-world datasets, our findings demonstrate that LLM-generated synthetic data can effectively prototype educational knowledge graphs, offering institutions a scalable, privacy-preserving framework for developing data-driven insights and decision-making capabilities.