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 paper 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, usability, and support of the system in endgame teaching.
Figure. The user interface of the execution phase. (a) Information about the endgame scenario and prompts for users to choose either the black or white side; (b) The recommended moves, move explanation and user-executable buttons; (c) Learning completion prompts and learning summary information.
Cite this work: Mingyu Zhang, Qiao Jin, Qian Dong, Danli Wang, Jun Xie. 2024, “EK-Chess: Chess Learning System Based on Top-Level Chess Expert Knowledge Graph”. International Journal of Human-Computer Interaction (accept with major revision).