Algorithmic Reading and Changing Interpretations of Contemporary English Literature

Abstract

The increasing use of large language models, semantic analytics, and computational text-processing systems is altering how literary texts are approached, interpreted, and discussed in academic environments.
This study investigates algorithmic reading not merely as an analytical aid but as an interpretive intervention capable of reshaping readers' perceptions of contemporary English literature. A central research gap lies in the limited empirical understanding of how AI-generated interpretive cues influence thematic plurality, textual evidence selection, ambiguity recognition, and independent critical reasoning. The proposed research therefore compares unaided human reading with algorithm-assisted interpretation across selected contemporary English literary texts representing culturally and stylistically diverse narrative forms.
It conceptualizes interpretive change through dimensions including semantic convergence, interpretive diversity, evidential grounding, contextual sensitivity, and reader–algorithm agreement. Rather than treating machine-generated interpretation as an objectively superior reading, the study examines where algorithmic assistance expands, narrows, or redirects literary meaning.
The proposed framework combines computational semantic modeling with human interpretive assessment to quantify patterns that conventional literary criticism generally evaluates qualitatively.