This book examines the transformations in medieval studies—and the humanities more broadly—enabled by decades of digitization and advances in computational methods. Centring on the Paris Bible, a widely copied thirteenth- and fourteenth-century manuscript genre, we demonstrate how automated transcription produces scribal data at a scale once inaccessible, and how automation can support new approaches to localizing, dating, and contextualizing manuscripts. We argue that bringing machine learning and artificial intelligence to medieval studies not only requires re-centring expert human intelligence within computational systems, but also raises the question of the infrastructures needed for equitable, collaborative scholarship across the field. The book models how medieval studies might rethink interpretation, highlighting both the promise and risks of computational methods in manuscript research.

This book examines the transformations in medieval studies—and the humanities more broadly—enabled by decades of digitization and advances in computational methods. Centring on the Paris Bible, a widely copied thirteenth- and fourteenth-century manuscript genre, we demonstrate how automated transcription produces scribal data at a scale once inaccessible, and how automation can support new approaches to localizing, dating, and contextualizing manuscripts. We argue that bringing machine learning and artificial intelligence to medieval studies not only requires re-centring expert human intelligence within computational systems, but also raises the question of the infrastructures needed for equitable, collaborative scholarship across the field. The book models how medieval studies might rethink interpretation, highlighting both the promise and risks of computational methods in manuscript research.