Starry Textscapes: Astronomy, Cultural Memory, and AI in Language and Literature A Critical Review

Abstract

This critical review examines the underexplored convergence of astronomical cultural memory and artificial intelligence in literary studies. Drawing on scholarship in cultural astronomy, memory studies, and AI literary analysis, it argues that both the night sky and algorithmic systems function as memory technologies that organize, transmit, and transform cultural narratives. Traditional constellation lore, from Mesopotamian, East Asian, and Indigenous traditions to medieval European literature has historically encoded cosmic order, social hierarchy, and collective identity into narrative form. Simultaneously, large language models have emerged as generative agents that destabilize authorship and introduce probabilistic mechanisms of textual production. Despite substantial activity in each domain, existing research treats them largely in isolation. To address this gap, the review proposes the concept of Celestial Algorithmic Memory (CAM), a tripartite framework of encoding, transformation, and reception that theorizes how astronomical narratives are digitized, statistically reproduced, and reinterpreted under algorithmic conditions. Methodologically, the study synthesizes peer-reviewed literature (2010–2026) through a systematic search strategy adapted from PRISMA-P principles. Findings highlight persistent divergences between human and AI-generated narratives in emotional depth, moral ambiguity, and cultural specificity, while underscoring the political stakes of training-data selection. The review concludes that as AI increasingly mediates literary production, critical attention to whose celestial narratives are preserved, mutated, or erased becomes an ethical as well as scholarly imperative. CAM thus offers an integrated lens for future interdisciplinary inquiry at the intersection of cultural astronomy, memory studies, and critical AI literary analysis.