Evaluation of a Recommender System for Assisting Novice Game Designers
This work addresses the challenge of improving productivity and creativity for novice game designers, though it appears incremental as it applies existing recommender system methods to a new domain.
The paper tackled the problem of assisting novice game designers by developing an AI-driven recommender system that suggests game mechanics, resulting in increased user accuracy and computational affect, and decreased workload.
Game development is a complex task involving multiple disciplines and technologies. Developers and researchers alike have suggested that AI-driven game design assistants may improve developer workflow. We present a recommender system for assisting humans in game design as well as a rigorous human subjects study to validate it. The AI-driven game design assistance system suggests game mechanics to designers based on characteristics of the game being developed. We believe this method can bring creative insights and increase users' productivity. We conducted quantitative studies that showed the recommender system increases users' levels of accuracy and computational affect, and decreases their levels of workload.