Pacing diagrams are used in game design practice to represent player experience as a temporal structure. However, the term currently refers to heterogeneous and informal artifacts, limiting both interdisciplinary communication and computational use. We propose a minimal formal reference description of pacing diagrams for linear player experience sequences. The contribution defines the core structural elements of the diagram and frames pacing diagrams as both communicative design artifacts and structured representations. This formalization provides a shared point of reference for design practice while enabling consistent interpretation by tools and computational systems. As a first step, it establishes a foundation for more comparable, interoperable, and analyzable representations of player experience in game design.

Design pillars are widely used in game development as high-level principles that define the intended player experience and guide design decisions. Despite their prevalence in industry practice, their role remains underexplored in academic literature. This paper provides a structured description of design pillars and investigates their use through an exploratory questionnaire study with game developers. The results show that design pillars are well understood and commonly used to support early design decisions and team alignment. However, their application is often inconsistent, declines over the course of development, and is challenged by vagueness, difficulty in operationalization, and insufficient documentation. These findings reveal a gap between the conceptual role of design pillars and their practical use. We discuss implications for game design practice and outline directions for future work toward more structured, player-experience-oriented design processes.

Game Design Pillars are natural language artifacts commonly used in game development to communicate a project’s core vision and ensure a coherent player experience. Their linguistic nature aligns well with the strengths of Large Language Models (LLMs), which excel at generating and interpreting natural language, making them promising candidates for supporting mixed-initiative pillar workflows. In this study, we introduce a formal definition of game design pillars, present an initial prototype—SPINE—and investigate the utility of LLMs in the creation and decision-making processes associated with pillar-driven workflows. We begin with a pre-study comparing gemini-2.0-flash and GPT-4o-mini. Results show that Gemini is better suited to our tasks due to greater output variety and consistency. We then conduct a case study by deploying the tool at a local game jam. Findings indicate positive reception and clear value in integrating SPINE into early-stage development. Finally, we interview four experts, demonstrating the tool and allowing them to experiment with it in a controlled environment. While individual perspectives vary, the overall perception is encouraging and supports our intuition: LLMs can meaningfully contribute to game design pillar workflows. These early findings highlight the potential of formalizing pillar-driven design as a research space and point toward several promising avenues for future work.

Designing pacing for video games presents a unique set of challenges. Due to their interactivity, non-linearity, and narrative nature, many aspects must be coordinated and considered simultaneously. In addition, games are often developed in an iterative workflow, making revisions to previous designs difficult and time-consuming. In this paper, we present PaceMaker, a toolkit designed to enable common design workflows for pacing while addressing the challenges above. We conducted initial research on pacing and then implemented our findings in a platform-independent application that allows the user to define simple state diagrams to deal with the possibility space of games. The user can select paths on the directed graph to visualize a node’s data in diagrams dedicated to intensity and gameplay category. After implementation, we created a demonstration of the tool and conducted qualitative interviews. While the interviews raised some concerns about the efficiency of PaceMaker, the results demonstrate the expressiveness of the toolkit and support the need for such a tool.