Constructing an ontology-based knowledge graph for K-12 computer science competency via human-AI collaboration

Competency is a broad concept in education, often described in unstructured natural language within curriculum frameworks, which makes assessing it difficult. The construction of a competency ontology serves to mitigate this drawback by illustrating its boundary and internal structure. However, construction of ontology is a very time-consuming work that need huge effort from several domain experts. Inspired by the powerful language understanding capability of large language models (LLMs) such as GPT model, this work proposed a human-AI collaboration approach to accelerate the construction of competency ontology. This paper demonstrates how to extract knowledge concepts, relations and entities from a Chinese national computer science curriculum framework to construct a competency ontology and ontology-based knowledge graph through collaboration with GPT model. We evaluate the extraction result of GPT model by comparing with manual result and other LLMs’ result. The outcomes of GPT-4o model demonstrate that it is capable to cover at least three-quarters of human efforts in extraction, showcasing its superior qualification in ontology and ontology-based knowledge graph constructions.