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ClueCart: Supporting Game Story Interpretation and Narrative Inference from Fragmented Clues

Xiyuan Wang, Yifan Cao, Junjie Xiong, Sizhe Chen, Wenxuan Li, J. Z. Zhang, Quan Li

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Abstract

Indexical storytelling is gaining popularity in video games, where the narrative unfolds through fragmented clues. This approach fosters player-generated content and discussion, as story interpreters piece together the overarching narrative from these scattered elements. However, the fragmented and non-linear nature of the clues makes systematic categorization and interpretation challenging, potentially hindering efficient story reconstruction and creative engagement. To address these challenges, we first proposed a hierarchical taxonomy to categorize narrative clues, informed by a formative study. Using this taxonomy, we designed ClueCart, a creativity support tool aimed at enhancing creators’ ability to organize story clues and facilitate intricate story interpretation. We evaluated ClueCart through a between-subjects study (N=40), using Miro as a baseline. The results showed that ClueCart significantly improved creators’ efficiency in organizing and retrieving clues, thereby better supporting their creative processes. Additionally, we offer design insights for future studies focused on player-centric narrative analysis.

Topics & Concepts

NarrativeInterpretation (philosophy)InferenceComputer scienceGame studiesArtificial intelligenceHuman–computer interactionNatural language processingLiteratureArtProgramming languageArtificial Intelligence in GamesDigital Games and MediaVideo Analysis and Summarization