From Archive to Algorithm: Artificial Intelligence, Memory, and the Future of Literary Interpretation

Publication Details

Publication Date

31/08/2026

Volume / Issue

Vol 1, Issue 4 (2026)

Article No.

004

Journal

British Journal of Contemporary Research

DOI

https://doi.org/10.67693/BJCR-Y674X85R

Received

08 Aug 2026

Views

34

Downloads

26

Abstract

The emergence of generative artificial intelligence has introduced a significant transformation in the ways literary texts are accessed, interpreted, classified, compared, and remembered. While earlier developments in digital humanities primarily expanded the archive and introduced computational methods into literary scholarship, generative artificial intelligence increasingly participates in the interpretive process itself. This paper examines the transition from the digital archive to the algorithmic interpretive environment and argues that artificial intelligence should be understood neither simply as a technological instrument nor as a substitute for the human reader, but as a new mediating layer between literary texts and their interpreters. Drawing upon digital humanities, posthumanism, reader-response theory, cultural memory studies, and critical algorithm studies, the paper investigates four interconnected transformations: the algorithmic expansion of the archive, the emergence of the AI-assisted reader, the redistribution of interpretive authority, and the politics of algorithmic memory. It further examines the implications of these transformations for literary studies in multilingual and postcolonial contexts, where questions of linguistic diversity, cultural representation, and archival exclusion are particularly significant. The paper argues that AI-assisted interpretation can enlarge the possibilities of literary scholarship by enabling large-scale comparison, pattern recognition, multilingual exploration, and new forms of textual inquiry. At the same time, algorithmic mediation can reproduce existing cultural hierarchies, obscure historical context, privilege dominant languages, and convert interpretive complexity into apparently authoritative summaries. The future of literary interpretation, therefore, requires neither technological rejection nor uncritical technological enthusiasm, but a critical model of human–AI collaboration grounded in contextual knowledge, interpretive plurality, transparency, and humanistic judgment.

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