Bridging the Divide: Saussurean Structure and Derridean Complexity in ChatGPT’s Meaning-Making

This study explores the central question: how does generative AI, particularly ChatGPT, align with or diverge from traditional literary theories represented by Saussurean structuralism and Derridean deconstruction in its approach to meaning generation? The aim is to conduct a comprehensive dual-framework analysis of ChatGPT, utilizing insights from scholarly articles to assess how it reproduces structured linguistic patterns and handles the complexities of contextual and interpretive fluidity. The study reveals that while ChatGPT excels in tasks requiring structural adherence, such as grammar refinement and essay structuring, it struggles with capturing the nuanced interpretive tasks that require an understanding of cultural and contextual variability. Significantly, this paper emphasizes the implications of these findings for integrating AI into educational frameworks, advocating for strategies that enhance AI literacy and adapt AI tools to diverse learning environments. The study’s insights contribute to the broader discourse on the role of AI in transforming knowledge production and interpretation, highlighting the need for nuanced integration that respects the complexities of human language and cultural diversity.