Design and Evaluation of a Question-Answering System Based on Knowledge Graph-Augmented Large Language Models in K–12 Artificial Intelligence Curriculum

Digital transformation is reshaping the education sector, fostering an AI-enabled, learner-centered ecosystem. This shift is characterized by the adoption of large language models (LLMs) in education, which is forging a new paradigm for intelligent teaching. However, the integration of LLMs into K–12 AI education is often hindered by their tendency to generate factually inaccurate and pedagogically misaligned content. To address this, we constructed a knowledge graph (KG) of the K–12 AI curriculum and developed a question-answering system based on KG-augmented LLMs. The system was evaluated on a dedicated AI curriculum dataset comprising 1098 questions categorized into three difficulty levels. The evaluation employed the G-Eval with no-reference metrics. Using DeepSeek-V3 as the scoring model, the system performance was assessed across three mainstream LLMs and measured along five distinct dimensions. Results indicated that the integration of curriculum KG significantly enhanced the factual accuracy and relevance of LLM-generated answers in K–12 AI education. However, this enhancement involves a trade-off, as the incorporation of non-declarative knowledge can negatively affect linguistic fluency and coherence. Performance gains varied across LLMs: Qwen and Baichuan demonstrated the strongest improvements, particularly in complex tasks. This study provides a scalable, knowledge-anchored framework for developing reliable AI teaching assistants, demonstrating a practical pathway to mitigate domain-specific hallucinations in educational applications.