Themed Research Seminar (Aesthetics, AI, Criticism, and Cultural Form) Session 4: Literary Criticism and AI: Interpretation as Practice, Simulation as Discourse

Session 4: Literary Criticism and AI: Interpretation as Practice, Simulation as Discourse 

Presented by: Dr Nick Lord

Date: Friday 15 May 2026
Time: 12-1pm
Location: Online via Zoom and in person at 09-738 (Level 7, Michie)

Title: Literary Criticism and AI: Interpretation as Practice, Simulation as Discourse

Abstract: This paper asks what artificial intelligence has to offer literary studies as a critical practice, rather than as a pedagogical tool. Situating large language models within a longer history of computation in literary studies, it argues that earlier computational approaches—stylometry, concordances, spatial modelling, and computational narratology—functioned primarily as representational tools that reframed qualitative data for interpretation. By contrast, large language models generate discourse that resembles—but is not identical with—the practice of interpretation

The paper develops this claim through two examples. E. M. Forster’s distinction between story and plot illustrates forms of causal inference that lend themselves to formalisation and computational modelling. A contrasting example from Henry James highlights rhetorical effects that unfold in the temporal experience of reading. Drawing on postclassical narratology, theories of reading (Iser, Barthes, Eco, Shklovsky), and hermeneutics (Dilthey, Ricoeur), the paper argues that literary criticism is best understood as a temporally unfolding interpretive practice in which explanation and understanding enter into a dialectical relation.

This account does not identify criticism with suspicious critique alone. Debates around surface reading and postcritique (Best and Marcus; Felski) suggest a broader field of critical practices—descriptive, rhetorical, affective, and evaluative—grounded in acts of reading. Large language models can simulate the discursive forms of these practices, but they compress the temporal process through which interpretive judgement emerges. The most productive point of contact between AI and literary criticism therefore lies in a heuristic relationship: AI generates interpretive possibilities, while criticism evaluates and develops them. The paper concludes by revisiting N. Katherine Hayles’s account of the synergy between machine and close reading, suggesting how it might be updated for the era of large language models.

Bio: Dr Nick Lord’s teaching and research focuses on twentieth-century literature, literary theory, and the history and politics of narrative form.