DIGITAL HUMANITIES AND ARTIFICIAL INTELLIGENCE IN CONTEMPORARY ENGLISH LITERARY STUDIES: METHODOLOGIES, EPISTEMOLOGIES, AND EMERGING RESEARCH PARADIGMS

Abstract

The integration of Digital Humanities (DH) methodologies and Artificial Intelligence (AI) frameworks is fundamentally reshaping contemporary English literature research, establishing a multidimensional analytical space where computational scalability intersects with hermeneutic interpretation. This review paper examines the epistemological transformation of literary studies through the convergence of quantitative text analysis, computational stylometry, cultural analytics, and generative AI applications. Drawing upon Actor-Network Theory (ANT) as a theoretical lens, the paper conceptualizes AI systems, natural language processing algorithms, and digital archives as non-human actors that dynamically interact with human scholars, publishing institutions, and creative authors within a complex literary-critical network. The review critically evaluates the paradigm of distant reading and computational formalism, addressing the epistemological controversies surrounding computational literary studies, particularly Nan Z. Da's critique regarding statistical robustness and empirical obviousness. Through comparative analysis of methodologies ranging from traditional close reading to generative AI co-creation, the paper demonstrates how computational tools enable macro-level historical analysis, authorship attribution, institutional auditing, and the detection of systemic inequalities such as "cultural redlining" in publishing. The emergence of post-transformer Large Language Models has expanded AI's role beyond corpus analysis into active narrative generation and creative collaboration, destabilizing traditional concepts of authorship, originality, and intentionality. Critical challenges examined include algorithmic bias, contextual flattening, hermeneutic reduction, and the neoliberal political economy of digital humanities. The paper concludes by advocating for methodological hybridity through a synergistic "close-distant" model and identifies future paradigms including Retrieval-Augmented Generation systems, immersive spatial technologies, and vector space probing that promise to reconfigure literary scholarship while preserving the hermeneutic depth essential to humanistic inquiry.