The Limits of AI in Teaching Partition Literature: A Critical Perspective on the Risks of Algorithmic Interpretation in Sensitive Historical Contexts

Abstract

The application of generative AI in the classroom is transforming conventional methods of literary analysis and instruction, but it also raises serious concerns and limitations. This chapter critically examines these limitations within the context of teaching 1947 Partition literature in Indian college classrooms. Using a qualitative and experimental methodology, the chapter analyzes AI-generated responses to Partition narratives, revealing ChatGPT's inability to capture the historical trauma, moral accountability, and cultural depth embedded in these texts. Findings show that AI-generated interpretations often flatten complex human experiences and reduces them to simplistic patterns or generalized tropes. The chapter argues that such algorithmic interpretations risk distorting historical memory and promoting academic irresponsibility. By exposing these flaws, the chapter contributes to current debates on AI in higher education and calls for human-led literary analysis in contexts marked by deep historical and cultural trauma.