Designing a Large Language Model-Based AI System for Dynamic Difficulty Adjustment in Digital Games

Abstract

This study investigates how large language models (LLMs) can serve as dynamic agents in game-based interactions by comparing two prototypes of a color-guessing game. One model (Cohere Command) operates on a zero-shot prompt-based mechanism, while the other (FLAN-T5) is fine-tuned on a semantically structured dataset. A total of 20 participants were divided into two experimental groups to evaluate the models' ability to generate semantically coherent yes/no questions, maintain flow, and perform accurate predictions. Quantitative data, including session durations, number of interactions, and AI outputs, were analyzed, along with a post-game user experience survey grounded in Flow Theory. Results show that while both systems achieved task completion, the fine-tuned FLAN-T5 model significantly outperformed the other models in terms of semantic clarity, user engagement, and perceived fluency. The findings highlight the potential of LLM-based DDA systems in creating meaningful, adaptive player experiences and underscore the importance of semantic alignment and interaction transparency in game-based AI design.

Keywords

• Artificial Intelligence
• Machine Learning
• Digital Game Design
• Dynamic Difficulty Adjustment

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