Collaborative Human-AI Generative Board Game Design and Simulation Using Agent-Based Modeling and Large Language Models: A Case Study of Confucian Odyssey: The Journeys of Confucius
Keywords:
大语言模型（LLMs）, Large Language Models (LLMs); Deepseek Model; Agent-Based Modeling (ABM); Board Game Design andSimulation; Human-AICollaborationandGame-BasedLearning 121, Deepseek模型, 代理人基模型（ABM）, 桌游设计与模拟, 人机协作与游 戏化学习Abstract
This study explores the application of Large Language Models (LLMs) and Agent-Based Modeling (ABM) in collaborative human-AI generative board game design and simulation, using Confucian Odyssey: The Journeys of Confucius as a case study. Using the Deepseek model and a P-T-R iterative workflow, the research integrates Confucian cultural elements with strategic gameplay. The board game features multiple victory paths that include cultural transmission, political expansion, and moral cultivation. ABM simulations reveal the influence of resource dynamics, moral choices, and player interactions on the evolution of the game, validating the balance and educational value of the game. Based on simulation analysis, the resource system and rules were further optimized. The results demonstrate that LLMs enhance the efficiency of concept generation and content development, while ABM provides quantitative validation. This study presents a new approach that combines generative AI and system modeling, offering practical insights into the integration of historical education with game-based learning.
