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
Published: 2025-12-03
Source type: essay
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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.
Pull quotes
Disciplines Blur Up Close
“And yet, observed closely, they blur.”
Teachers Need Frameworks
“Yet students need something to work with. Teachers need frameworks that function on Tuesday morning.”
Strategic Simplification
“Not imaginary, but selective. Not false, but incomplete.”
Maps, Not Territories
“Maybe the goal isn’t to resolve this tension but to inhabit it productively”
Big ideas
- AI literacy has to be taught inside real subjects
- Students need to bring the purpose; AI should not supply it for them
Claims
- Subject-specific AI literacy frameworks are useful maps, not final answers
- AI literacy only works when it is connected to subject-area knowledge
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.
Durability note
This is durable as a framing piece for disciplinary AI literacy: later frameworks may refine the categories, but the practical tension between useful classroom maps and messy disciplinary territories will remain.