Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Bisht, Priyanka; Pujari, Jyoti Prakash Title A name given to the resource The limits of AI in teaching partition literature: A critical perspective on the risks of algorithmic interpretation in sensitive historical contexts Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Pitfalls of AI Integration in Education: Skill Obsolescence, Misuse, and Bias;pp.49-72 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3373-0122-8.ch003" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-0122-8.ch003</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010396714?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010396714?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Bisht P., Christ University, India; Pujari J.P., Christ University, India Description An account of the resource 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. 2025 by IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-833730124-2; 979-833730122-8; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online