A Computational Narratological Study of Achebe’s Things Fall Apart Using Generative AI

Abstract

The objective of this research is to analyze Things Fall Apart by Chinua Achebe in the context of computational narratology with generative artificial intelligence. It explores the way AI models work with, interpret and reproduce cultural, cognitive and structural aspects inherent in postcolonial African stories. This research employs close reading approach alongside AI driven textual modeling to critically evaluate the understanding and re-creation of postcolonial narratives through the lens of generative AI. This research seeks to determine whether these systems are able to recognize and replicate the cognitive and sociocultural touches of Igbo traditions of storytelling. This research explores computable representation and how these elements may be represented or produced in computation. The research investigates the degree to which generative AI is able to reproduce or modify the socially dominant epistemes and narrative logics of decolonial Achebean literary discourse. This research adds to the growing body of knowledge within the digital humanities and postcolonial studies.

Keywords: Generative AI, Narrative Structure, African Literature, Narrative Theory, AI and Storytelling, Oral Tradition, Literary AI, Decolonizing AI