Ars Inquirendi 2025 and 2026 Conferences:
Querying Pre-Print Cultures with LLMs
St Edmund Hall, Oxford · Online
20–22 November 2026
Following the first Ars Inquirendi conference in December 2025, we invite proposals for a second hybrid exploration of the impact of Large Language Models (LLMs) on the study of cultures before the dominance of movable-type printing (for example, Western Europe up to the sixteenth century, Russia the eighteenth, or Central Asia the late nineteenth).
Even in the few months since that first conference, the ground has shifted dramatically. New LLM techniques — more powerful sometimes by the week — are allowing individual scholars, without programming experience, to bring new objects of study to light within the pre-print-era archive: objects previously beyond the reach of even lavishly endowed projects. The inferential prowess of LLMs is especially promising for pre-print cultures, since their archives are overwhelmingly one of loss, dispersal and uneven survival. Even the old problem of machine transcription of handwriting from any era, far harder than for print, is now advancing rapidly.
Hearteningly, we are also learning more about the grounding of these systems in the core interests of the humanities. LLMs are language-shaped machines, trained on human expression and responsive to all the breadth and detail of natural language. Some of the most striking new techniques for eliciting powerful behaviour from models depend not on esoteric programming, but on skills such as rhetoric, philology, hermeneutics, poetics, psychology, and the art of asking. This is renewing perceptions of the humanities’ importance across our economies, institutions and shared intellectual life.
However, pre-print-era scholarship is also exceptionally vulnerable to the floods of synthetic text, shallow automation, and dangerously plausible AI nonsense that we all see, inside and outside the academy. Not only are its extant materials just a fraction of what existed; only a fraction of those materials fall within the data of current models, which train overwhelmingly on post-print production, above all twenty-first-century internet culture, to build their ever more convincing worlds.
Ars Inquirendi 2026 therefore invites contributions, from papers to workshops, that explore not only how scholars should use LLMs, but also shape the entire means by which the pre-print past becomes machine-readable.
