Literary works present diverse and complex character behaviors, often implicit or intentionally obscured, making character analysis an inherently challenging task. This study explores LLMs’ capability to identify and interpret behaviors of artificial beings in 11 award-winning contemporary Korean science fiction short stories. Focusing on artificial beings as a distinct class of characters, rather than on conventional human characters, adds to the multi-layered complexity of analysis. We compared two LLMs, Claude 3.5 Sonnet and GPT-4o, with human experts using a custom eight-label system and a unique agreement metric developed to capture the cognitive intricacies of literary interpretation. Human inter-annotator agreement was around 50%, confirming the subjectivity of literary comprehension. LLMs differed from humans in selected text spans but demonstrated high agreement in label assignment for correctly identified spans. LLMs notably excelled at discerning ‘actions’ as semantic units rather than isolated grammatical components. This study reaffirms literary interpretation’s multifaceted nature while expanding the boundaries of NLP, contributing to discussions about AI’s capacity to understand and interpret creative works.