Year: 2026
Type: article
Abstract: The integration of generative AI into humanities research presents both new opportunities and significant methodological challenges. While generative AI enables more sophisticated textual, visual, and historical analysis than ever before, it also introduces novel methodological risks. Prompts now function as measurement instruments, construct validity is entangled with model fluency, the increasing abstraction and subjectivity of tasks challenge single-answer evaluations, small changes in prompt or model configuration can materially alter substantive conclusions, and model training encodes strong theoretical assumptions prior to human interaction. This article advances a theory-first framework for generative-AI-assisted humanities research that traces how theoretical commitments intervene across the research lifecycle. Drawing on examples from the computational humanities and social sciences, it argues that theory operates as methodological infrastructure that constrains degrees of freedom, supports reproducibility, and enables principled iteration. By reframing generative AI as a site of theoretical inscription, the article outlines practical strategies for building robust and theoretically grounded humanities research in the era of generative AI.
Author: Andrew Piper
Institution: McGill University
Language: English
Subfield: Literature and Literary Theory
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $3,550
OpenAlex ID: W7165157916
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