Beyond the Hype: Why Your School’s AI Literacy Strategy Needs System Altitude
Source: Educating AI / Nick Potkalitsky Substack
Author: Nick Potkalitsky
Original source: https://nickpotkalitsky.substack.com/p/beyond-the-hype-why-your-schools
Private backup: the full article text is archived in the private repository at archives/articles/nickpotkalitsky-substack-com-beyond-the-hype-why-your-schools.source.md. It is not published on the public Quartz site.
Summary
Potkalitsky argues that schools are using the phrase “AI literacy” too broadly, collapsing very different AI interactions into one category. He proposes “system altitude” as a framework for distinguishing high-altitude open student use of general AI tools, mid-altitude educational AI spaces, and low-altitude closed instructional systems such as adaptive learning platforms. The article argues that most institutional investment clusters at low altitudes because those tools are measurable, structured, and easier to justify, while students are already operating at high altitudes informally and unevenly. Potkalitsky recommends a portfolio approach in which schools intentionally scaffold students from structured AI use toward autonomous judgment, using different assessment logics at different altitudes.
Big ideas
- AI literacy requires different kinds of AI interaction
- District AI work is a long-term redesign project
- AI literacy has to be taught inside real subjects
Claims
- AI literacy takes system capacity, not just tool access
- Schools need a mix of structured and open-ended AI experiences
Key evidence and examples
- High altitude includes independent student use of ChatGPT, Claude, or Gemini for homework, research, brainstorming, coding, or creative work.
- Mid altitude includes school-authorized general AI tools and teacher-governed pre-prompted assistants.
- Low altitude includes adaptive learning platforms, automated assessment, and structured tutoring systems.
- The article maps different assessment evidence to each altitude, from mastery metrics to artifacts, reflection, process patterns, decision journals, case analysis, and transfer indicators.
Education relevance
Very high relevance for district AI strategy, curriculum design, AI literacy frameworks, assessment redesign, procurement decisions, and equity planning.