University AI Infrastructure / Infophilia Tools

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University AI Infrastructure

Questions for teaching and research in the age of institutional AI.

A critical reading of how a university presents artificial intelligence to its community — and of the questions that presentation leaves unasked. It begins with the University of Illinois generative-AI app catalog and treats it as infrastructure worth reading, beyond a mere menu of tools. This page is an early, evolving note (v0.1), and an argument that I hope will grow.

Reading the AI-app catalog as infrastructure

The University of Illinois AI-app catalog presents AI primarily as working infrastructure — chat assistants, meeting tools, and AI embedded in the Microsoft, Google, Zoom, and GitHub ecosystems, with tiered access based on university and data classification. It carries meaningful cautions. Yet students and faculty are treated unevenly, and intellectual labor risks being reframed as a set of “productivity tasks” to be accelerated, governed, and purchased.

A university is also a place for independent thought, slow reading, and non-commercial infrastructure. Reading the catalog as infrastructure — rather than as a neutral list of apps — is a way to keep those commitments in view as AI is woven into everyday academic work.

Source: University of Illinois. (n.d.). Generative AI — AI apps. See also University of Illinois Library. (n.d.). Generative AI for research: Critical thinking and AI.

Questions a stronger presentation would ask

Alongside the apps, four questions worth keeping open:

  • What should never be automated? Critical thinking, ethical reasoning, and creative problem-solving are not obvious candidates for delegation.
  • What forms of learning require authorship and struggle? Close and deep reading, and interdisciplinary synthesis, may depend on the effort AI is designed to remove.
  • Who owns and can inspect the infrastructure of knowledge production? The systems that shape access to knowledge should be open to scrutiny, contest, and correction.
  • How will the university protect scholarly autonomy rather than simply optimize academic workflows? Efficiency and autonomy are not the same goal, and can pull in opposite directions.