Using Artificial Intelligence in board game beta testing

Undoubtedly, artificial intelligence is rapidly pervading our daily lives, from entertainment to professional applications. We are immersed in a technological context where the competency to effectively utilize diverse tools is essential for optimizing our actions and outcomes. In this post, I will discuss the methodology I employed in using AI for the beta testing phase of my last two board game projects.

First and foremost, I must clarify that my application of AI is not a substitute for creative effort. I avoid using lazy or generic prompts such as, "create a card game with Roman Empire theme using trick track mechanics." I consider this approach to be ineffective, as it would likely yield a generic, derivative product lacking a distinct core concept or "soul."

Instead, I employ AI in a pragmatic and iterative manner. The process begins with establishing the game's core concept, writing the comprehensive rulebook, and compiling a complete list of assets (board, components, dice, etc.). This initial stage results in a prototype ready for preliminary testing. However, prior to testing with human players, I utilize Manus AI to conduct a complete virtual beta test session.



The process is straightforward: I submit the rulebook (PDF) and the file detailing the game pieces (PDF). I then provide the initial prompt: "Manus, I request you to analyze the content regarding my new game. Upon completion of this analysis, please identify any aspects that are not completely unambiguous or 'crystal clear'." After addressing the system’s initial queries, the final prompt is executed: "I now request 100 simulations encompassing two, three, and four players. The required output is a detailed feedback report assessing game balance, pointing out potential rule errors, and suggesting improvements for the core mechanics." A significant advantage of Manus is its capability to generate Python programming code to facilitate testing on external platforms beyond the application itself.

It is crucial to note that testing with human players will always remain a fundamental part of the development cycle. Nevertheless, the initial results demonstrate that the prototypes are significantly more refined before the first human playtest. For example, in a recent beta testing session for my new game, the prototype was substantially more polished prior to the initial live test. The overall iteration process becomes considerably more efficient when AI is applied judiciously.

I am currently refining this methodology and anticipate being able to share further insights and impressions soon.

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