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This research study explores the emotional responses evoked during game play and interactions with non-player characters (NPCs) in a mystery-solving game. Structured around three phases-introduction, collaboration, and feedback-, the game uses large language models (LLMs) to simulate varying emotional states in NPCs, from neutrality to expressions of anger, joy, and more. Players’ emotional responses to the game play and interaction with the NPCs are captured through the dialogue they input. Language models are used to extract emotion scores from these in-game conversations. This study aims to enhance our understanding of emotional dynamics within gaming environments, in order to further inform the design of emotionally engaging experiences. Additionally, it underscores the potential of language models for scientific inquiries in human-computer interaction.
Marincioni et al. (Mon,) studied this question.