Quest of Aivengarde: Comparative Study of Player Experience Across LLM Dialogue Systems

Abstract

Non-player character (NPC) dialogue plays a crucial role in games. Narrative-driven video games in particular depend on NPCs to help shape the player experience by contributing to narrative, immersion, and player agency. Current technologies allow large language models (LLMs) to create dynamic, context-sensitive dialogue for NPCs, yet their impact on player experience remains underexplored. Quest of Aivengarde is a custom-built role-playing game developed as a research testbed for comparing four dialogue system designs: a static control version and three LLM-driven variants that rephrase, hybridize, or fully generate NPC dialogue. Building on previous pilot and demo studies, this paper integrates the system design and full empirical evaluation to examine how levels of generative agency affect interaction quality, immersion, and player engagement. Using mixed-methods analysis of behavioral logs and post-game surveys from 64 participants, results suggest that fully open-ended LLM dialogue fosters longer, more natural conversations and is particularly engaging for casual players. We discuss design trade-offs between narrative control and conversational freedom and propose a practical framework for selecting and combining LLM-driven dialogue approaches in future game development and research.