EK-Chess: Chess Learning System Based on Top-Level Chess Expert Knowledge Graph

Chess, as a form of intellectual sport, has garnered significant attention from researchers, driving continuous research into computer-assisted player training. However, contemporary teaching or training models frequently confine learners to passive observation of computer-generated results. Beginners may find it challenging to comprehend the cognitive processes underlying decision-making. To address this issue, this article proposes EK-Chess, a knowledge graph-based chess teaching system that encompasses a series of endgame teaching scenarios. This system assists chess beginners in learning the positional evolution in pawn endgames, helping users comprehend offensive and defensive strategies in endgames. User studies validate the effectiveness, and support of the system in endgame learning.

This paper introduces a chess teaching tool that uses a knowledge graph built from expert-level play to help beginners understand the reasoning behind moves, rather than just showing them computer-recommended answers. User studies show the system effectively supports learners in grasping endgame strategies by making expert thinking patterns visible and interactive.