EXPLAINING INTERTEXTUALITY THROUGH AI

This article argues that AI-assisted intertextuality should be judged by two linked capacities: the capacity to detect textual relations and the capacity to explain why those relations matter. Its originality is methodological rather than infrastructural: it proposes a constrained division of labor in which deterministic tools discover candidate textual relations, retrieval-augmented generation (RAG) explains them, and philologists adjudicate the claim. The test case is Coptic monastic literature, especially the writings of Shenoute and Besa, leaders of the White Monastery federation in late antique Egypt. Their works are difficult for non-specialists because Coptic requires segmentation, biblical quotations are often transformed, and scriptural language functions as social authority rather than ornament. Building on Miyagawa’s TRACER-based dissertation and recent THOTH.AI experiments, the article compares TRACER, passim, and RAG. TRACER and passim remain necessary for reproducible large-scale discovery of quotations and near quotations. RAG-based AI contributes differently: it can retrieve lexical and textual evidence, translate and segment Coptic, explain altered wording, and make allusive hypotheses explicit. The article proposes a conservative hybrid workflow: deterministic tools discover candidates, RAG explains them under system-level constraints, and philologists validate the final claim.