GNNetic: Unpacking Player Decisions in Multiplayer Card Games to Drive Interface Design Improvements and Personalized Skill Enhancement: An Online Rummy Case Study

Player decision-making in multiplayer card games presents opportunities to improve human interaction with complex systems. We introduce GNNetic , a Relational Graph Convolutional Network for game analysis in card games. By encoding card relationships and game context as a graph, GNNetic frames each discard choice in Rummy (case study) as a graph-classification task learned from expert play, enabling the identification of players’ action deviations. We leverage these insights to address core HCI challenges in game design, interface optimization, and player development. GNNetic helps surface recurring patterns of suboptimal play that may indicate design-induced errors, informing iterative refinements to enhance usability and reduce cognitive load during gameplay. Our analysis and summative study demonstrate this potential: UI enhancements informed by GNNetic ( Companion Mode in practice games) reduce common errors like joker discards by ~31% and invalid declarations by ~27% . We also propose Skill Arena —a post-game visualization that renders these deviations accessible, providing feedback to foster metacognitive awareness and facilitate skill enhancement. GNNetic achieves a ~15x reduction in model size with ~6.5% accuracy improvement over larger baselines. By bridging expert strategy with interface design, this work paves the way for more intuitive UIs and targeted skill enhancement in card games, demonstrating tangible performance gains through interface improvements.