Ruled by the Representation Space: On the University's Embrace of Large Language Models

TL;DR

This paper interrogates how rapid university adoption of large language models (LLMs) could erode institutional autonomy by letting a heteronomous AI field set norms for teaching and research. It develops the concept of a representation space and the virtual/physical 'world-making' effects of LLMs, distinguishing a quantitative virtuality tethered to data from qualitative possibilities that challenge the 'given'. The author argues that generative AI's normative rationality—rooted in data-derived valuations and alignment processes—reframes learning as evaluation of model outputs rather than active meaning-making, with significant implications for pedagogy and scholarship. To prevent surrender to external norms, the paper calls for a critical framework that analyzes technology's material-discursive effects and defends plural virtualities within the University.

Abstract

This paper explores the implications of universities' rapid adoption of large language models (LLMs) for studying, teaching, and research by analyzing the logics underpinning their representation space. It argues that by uncritically adopting LLMs, the University surrenders its autonomy to a field of heteronomy, that of generative AI, whose norms are not democratically shaped. Unlike earlier forms of rule-based AI, which sought to exclude human judgment and interpretation, generative AI's new normative rationality is explicitly based on the automation of moral judgment, valuation, and interpretation. By integrating LLMs into pedagogical and research contexts before establishing a critical framework for their use, the University subjects itself to being governed by contingent, ever-evolving, and domain-non-specific norms that structure the model's virtual representation space and thus everything it generates.