Large Language Models in Game Development: Implications for Gameplay, Playability, and Player Experience

2026-03-29

AI Summary

This study investigates how large language models (LLMs) are reshaping game design paradigms and influencing gameplay, playability, and player experience. Through two LLM-integrated game projects, the research employs a collaborative autoethnographic approach, combining development practice, reflective narratives, and qualitative content analysis to systematically examine the role of LLMs within game architectures. The work provides the first empirical evidence that LLMs significantly enhance content diversity and personalization, yet simultaneously introduce novel challenges concerning factual accuracy, difficulty balancing, and narrative coherence. Building on these findings, the study proposes new architectural principles and quality evaluation dimensions specifically tailored for generative AI–driven games.

Abstract

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.

Problem

• Large Language Models
• Gameplay
• Playability
• Player Experience
• Generative AI

Innovation

• large language models
• game development
• player experience
• generative AI
• playability