Player Modeling and Large Language Models: An Interaction-Based Approach - Souza, Carlos H. R.; Berretta, Luciana O.; Carvalho, Sérgio T. (Anais do Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames); 2025: Anais do XXIV Simpósio Brasileiro de Jogos e Entretenimento DigitalDO - 10.5753/sbgames.2025.10174, 2025)
Introduction: Adaptive games increasingly demand methods to understand and personalize player experiences. Objective: This study explores the integration of Large Language Models (LLMs) into Player Modeling, leveraging in-game interaction data to analyze player states, predict reference values, and identify dynamic player profiles. Methodology: We propose a Player Model that harnesses the reasoning capabilities of LLMs to generate these insights, enabling adaptive gameplay and personalized player experiences. To demonstrate feasibility, we implemented a proof of concept within the Unity Engine and tested it in a custom-designed game. The evaluation employed G-Eval, an LLM-assisted framework, focusing on the correctness of outputs. Results: The results showed a high average correctness score of 4.044 out of 5.000 across 50 input-output pairs, with 75% of respondes scoring 4 or higher. These findings suggest that LLMs can support Player Modeling, offering a foundation for dynamic difficulty adjustment and personalized game design.