Large Language Models in Game Development: Implications for Gameplay, Playability, and Player Experience
Provides preliminary empirical insights for game developers and researchers on the implications of generative AI in game design.
This study examines how integrating large language models into game development affects gameplay, playability, and player experience, finding increased variability and personalization but challenges with correctness, difficulty calibration, and structural coherence.
This paper investigates how the integration of large language models influences gameplay, playability, and player experience in game development. We report a collaborative autoethnographic study of two game projects in which LLMs were embedded as architectural components. Reflective narratives and development artifacts were analyzed using gameplay, playability, and player experience as guiding constructs. The findings suggest that LLM integration increases variability and personalization while introducing challenges related to correctness, difficulty calibration, and structural coherence across these concepts. The study provides preliminary empirical insight into how generative AI integration reshapes established game constructs and introduces new architectural and quality considerations within game engineering practice.