Title: Leveraging AI for Simulation-Driven Research in Literary Studies

Abstract:

Generative artificial intelligence (AI) introduces novel opportunities for experimental approaches within literary studies, specifically through the implementation of controlled, grounded, large-scale, and cost-effective simulations of cultural production. While existing systems have not yet demonstrated the capacity to generate high-quality, book-length narratives that consistently adhere to arbitrarily defined cultural constraints or stylistic markers, significant research underpins each component necessary for literary-historical simulation. Key areas of existing inquiry include the validation and application of AI systems as proxies for distinct, differentiable human populations; the analysis of narrative and stylistic attributes in AI-generated content; the maintenance of stability and coherence in multiagent, multiturn simulations of human behavior; and technical methodologies for predictably modifying the knowledge and actions of generative models. Collectively, these domains offer a foundational framework for more extensive AI-driven modeling of literary production systems. This paper outlines the potential and obstacles associated with simulation-based experiments in literary studies, reviews the current technological landscape in related fields, and clarifies essential technical elements. To illustrate relevance for literary scholars, we report findings from experiments on literary text generation, contrasting them with prestigious, human-written novels. Notably, these results feature the inaugural demonstration of limited, in-distribution outputs from AI models within this specific context. The discussion concludes with a roadmap for future research focused on comprehensive counterfactual literary-historical simulations utilizing AI.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC