Preserving Human Judgment in AI-Assisted Research: An Exploratory Cross-Institutional Study of University Governance, Ancient-Text Interpretation, and the Scalable Oversight Problem

Abstract

Generative artificial intelligence is moving beyond editing and search assistance into substantive research activities involving interpretation, synthesis, translation, hypothesis development, data analysis, and scholarly writing. This creates a governance problem broader than hallucination: researchers may remain formally responsible for scholarly conclusions while increasingly delegating the reasoning required to evaluate those conclusions. This working paper examines that problem through an exploratory comparative document analysis of ten research universities selected for two characteristics: publicly documented engagement with generative AI and substantial relevance to archaeology, ancient languages, epigraphy, manuscripts, or specialist textual interpretation. Five institutions are located in World Bank high-income economies and five in lower-middle-income economies. Across both groups, institutional guidance repeatedly emphasizes human responsibility, independent verification, critical judgment, transparency, and preservation of researcher expertise. Several lower-middle-income cases additionally foreground access, training, institutional capacity, and AI literacy. Independent empirical research on generative AI and critical thinking provides partial support for concerns about cognitive offloading and dependence, while also showing that structured, reflective use can support learner agency. Ancient-language research offers a useful stress environment because fluent AI output can coexist with incomplete, unsupported, or incorrect interpretation. The paper therefore proposes human-first, evidence-bounded review as a testable oversight architecture and considers its relationship to the broader scalable-oversight problem associated with increasingly capable and potentially transformative AI systems.