PREFER: From Player Experience to Personalized Game Refinement

arXiv cs.AIOctober 15, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2610.19213v1 Announce Type: new Abstract: Understanding how large language models (LLMs) evaluate gameplay experience is important for using their feedback to guide game design. However, overall ratings alone provide limited insight into which aspects of experience are associated with these judgments and how they can inform concrete game changes. We introduce PREFER, a framework that uses a shared set of experience dimensions adapted from established psychometric questionnaires to benchmark how models evaluate gameplay and to guide persona-specific game refinement. Through these dimensions, we characterize the perspectives models offer as virtual playtesters by examining how their over

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