My Kids Do Long Division by Hand
Source: Nate’s Newsletter
Author: Nate’s Newsletter
Published: 2026-02-28
Source type: essay
Original source: https://natesnewsletter.substack.com/p/my-kids-do-long-division-by-hand
Private backup: the full article text is archived in the private repository at archives/articles/natesnewsletter-substack-com-my-kids-do-long-division-by-hand.source.md. It is not published on the public Quartz site.
Summary
The author argues that children need both traditional cognitive foundations and deliberate AI fluency. Long division by hand, physical books, handwriting, and independent struggle are not obsolete in an AI-rich world; they build the mental models needed to direct, evaluate, and challenge AI outputs. The article frames AI as analogous to calculators but broader: the answer is neither banning AI nor handing it over uncritically, but sequencing AI use after foundational practice. It emphasizes specification quality, metacognition, productive friction, and the ability to catch AI errors as core educational skills.
Pull quotes
Foundation before tool
“Build the foundation and then give them the tool.”
The struggle is the point
“The struggle is the point.”
Thinking comes first
“Claude doesn’t get asked first.”
Big ideas
- Learning still needs some struggle, even when AI can make things easier
- Students need to bring the purpose; AI should not supply it for them
- AI literacy has to be taught inside real subjects
- AI literacy should help people notice how AI changes what counts as knowing
- AI simulations need clear boundaries for learning
- Education should teach thinking with, without, and about AI
Claims
- Learning requires some productive struggle that AI can remove
- Students need boundaries for when to use AI and when to step back
- AI literacy only works when it is connected to subject-area knowledge
- Prompting AI is a literacy practice, not just a technical trick
- AI can undermine learning when students use it without guidance
Key evidence and examples
- The author contrasts a child doing long division by hand with the same child learning to vibe code with Claude.
- The calculator analogy argues that tools can be valuable after students learn underlying mechanics.
- Examples include AI tutoring, a wrong Claude math answer requiring human sanity checking, and vague versus precise vibe-coding prompts.
- The article’s principles include foundation before leverage, specification as literacy, director not passenger, sequence the autonomy, catch the machine, build not browse, and attempt before augmenting.
Education relevance
Very relevant for K–12 AI literacy, parenting, curriculum design, assignment sequencing, AI tutoring, and classroom norms that balance foundational skills with responsible AI use.
Durability note
The specific coding tools may change, but the foundation-first principle is durable for AI, calculators, and other learning technologies that can bypass practice.