In Search of a Foundation for Disciplinary AI Literacy

Source: Nick Potkalitsky Substack
Author: Nick Potkalitsky
Original source: https://nickpotkalitsky.substack.com/p/in-search-of-a-foundation-for-disciplinary

Private backup: the full article text is archived in the private repository at archives/articles/nickpotkalitsky-substack-com-in-search-of-a-foundation-for-disciplinary.source.md. It is not published on the public Quartz site.

Summary

Nick Potkalitsky reflects on the difficulty of building discipline-specific AI literacy frameworks when disciplines themselves are contested and internally diverse. He argues that frameworks such as DSAIL cannot fully capture the complexity of history, science, math, or literature, but can still provide strategic reductions: provisional maps that help teachers and students evaluate AI outputs against disciplinary purposes and standards. The point is not to make the framework final, but to make it usable enough that classroom practice can test, stretch, and revise it.

Big ideas

Claims

Key evidence and examples

  • The article describes disciplines as institutionally real but fuzzy under close inspection.
  • History serves as an example of a field with multiple valid practices, including archives, evidence, interpretation, microhistory, genealogy, and quantitative social science.
  • Potkalitsky compares DSAIL’s simplification to teaching “the scientific method”: incomplete, but pedagogically useful for novices.
  • The framework helps students ask whether AI output mimics disciplinary form while violating disciplinary substance.
  • Breakdowns in the framework are treated as productive evidence about both the discipline and the tool.

Education relevance

This is highly relevant to K-12 AI literacy, curriculum design, disciplinary literacy, and teacher professional learning because it gives educators permission to use practical frameworks without pretending that they fully define a discipline.

My notes