Eternagram: Probing Player Attitudes in Alternate Climate Scenarios Through a ChatGPT-Driven Text Adventure
<|reference_start|>Eternagram: Probing Player Attitudes in Alternate Climate Scenarios Through a ChatGPT-Driven Text Adventure: Conventional methods of assessing attitudes towards climate change are limited in capturing authentic opinions, primarily stemming from a lack of context-specific assessment strategies and an overreliance on simplistic surveys. Game-based Assessments (GBA) have demonstrated the ability to overcome these issues by immersing participants in engaging gameplay within carefully crafted, scenario-based environments. Concurrently, advancements in AI and Natural Language Processing (NLP) show promise in enhancing the gamified testing environment, achieving this by generating context-aware, human-like dialogues that contribute to a more natural and effective assessment. Our study introduces a new technique for probing climate change attitudes by actualizing a GPT-driven chatbot system in harmony with a game design depicting a futuristic climate scenario. The correlation analysis reveals an assimilation effect, where players' post-game climate awareness tends to align with their in-game perceptions. Key predictors of pro-climate attitudes are identified as traits like 'Openness' and 'Agreeableness', and a preference for democratic values.<|reference_end|>
@article{zhou2024eternagram:,
    title={Eternagram: Probing Player Attitudes in Alternate Climate Scenarios
  Through a ChatGPT-Driven Text Adventure},
    author={Suifang Zhou, Latisha Besariani Hendra, Qinshi Zhang, Jussi
  Holopainen, RAY LC},
    journal={arXiv preprint arXiv:2403.18160},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.18160},
    primaryClass={cs.HC}
}