Adaptive Knowledge Graph Game: A Large Language Model-Driven Framework for Personalized E-Learning

Knowledge graphs have emerged as a promising approach for representing educational content and supporting personalized learning. However, many existing knowledge graph-based e-learning systems rely on manually constructed domain models that require substantial expert effort and provide limited support for adaptive learning experiences. At the same time, gamification has been shown to improve learner engagement, but educational games often lack structured semantic representations of learner knowledge. This paper presents an adaptive knowledge graph game that integrates Large Language Models (LLMs), knowledge graph-based learner modeling, and game-based learning within a unified educational framework. The proposed system uses an LLM to generate a domain knowledge graph representing concepts and prerequisite relationships within a learning domain. Learner interactions are used to construct a learner knowledge graph that captures concept mastery and learning progress. By comparing the learner model with the domain knowledge graph, the system identifies potential knowledge gaps and generates adaptive learning quests targeting concepts that require further development. The framework incorporates game elements including quests, non-player characters, progression mechanics, and knowledge-based challenges designed to support engagement throughout the learning process. A prototype implementation was developed to demonstrate the integration of knowledge graph generation, learner modeling, gap analysis, adaptive quest generation, and gamified learning mechanics. This work contributes a practical framework for combining LLM-assisted knowledge graph construction with personalized educational gameplay and provides a foundation for future research on intelligent and adaptive learning environments. Unlike conventional graph-based educational systems that store learner information in proprietary graph databases or application-specific structures, AKGG employs Semantic Web technologies (RDF, OWL, and SPARQL) to provide transparent, explainable, and interoperable learner modeling. Adaptive learning decisions can be traced directly to explicit semantic relationships, mastery scores, and SPARQL query results, improving the explainability and auditability of educational recommendations. We further report a selection-policy ablation showing which parts of the adaptation mechanism actually determine what a learner is asked to do: the multi-term priority score changes the selected concept in a minority of decisions relative to a one-term mastery baseline, one of its five terms is provably inert under the current thresholds, and simulated session length is shown analytically to be invariant to the selection policy under the simulator’s assumptions, so the simulation cannot distinguish the policies on that outcome. The classroom deployment reported here is a feasibility pilot; no learning-outcome advantage is claimed.