Towards a Taxonomy of AI in Hybrid Board Games

Hybrid board games, which integrate digital and physical modalities, represent an evolving intersection of technology and traditional tabletop play. The primary objective of this research is to establish a foundational taxonomy that structures existing knowledge regarding artificial intelligence within these systems. By categorizing AI through specific lenses—including physical domain awareness, temporal involvement, and gameplay centrality—the framework seeks to move beyond rudimentary digital implementations and identify unexplored design spaces for future development.

The analysis identifies five core dimensions that define how AI functions within a hybrid environment. These range from simple, utilitarian state-tracking tasks to complex, adaptive roles where the AI acts as a dynamic game master. Examples such as Anki Overdrive and Room Racers illustrate these varying levels of sophistication, demonstrating that while many current applications focus on substituting manual record-keeping, there is significant untapped potential for AI to serve as a collaborative partner. By shifting the focus from mere automation to creative, interactive design, developers can better leverage AI to enhance the overall player experience.

This conceptual foundation provides a necessary starting point for classifying the diverse roles AI plays in modern tabletop gaming. While the current scope focuses on defining these taxonomic lenses to categorize existing mechanics, the findings emphasize that further empirical research is required to evaluate the quality of these implementations. Ultimately, this framework serves as a call to action for both academics and practitioners to move toward more innovative, AI-driven mechanics that fully exploit the unique possibilities of the hybrid medium.