Abstract
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.
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