How to Model Effective AI Use in Classrooms
Source: Mike Kentz Substack
Author: Mike Kentz
Original source: https://mikekentz.substack.com/p/how-to-model-effective-ai-use-in
Published: 2026-02-04
Source type: essay
Private backup: the full article text is archived in the private repository at archives/articles/mikekentz-substack-com-how-to-model-effective-ai-use-in.source.md. It is not published on the public Quartz site.
Summary
Mike Kentz argues that prompt-engineering acronyms and starter frameworks are insufficient because effective AI use is context-specific, iterative, and language-based. He proposes teaching AI interaction through writing pedagogy: students compare stronger and weaker chat transcripts, annotate them, discuss what makes one better, and co-create criteria for better use. This treats AI chats as a new genre of text rather than a technical trick. Kentz also notes unresolved questions about transcript design, facilitation, transfer beyond humanities, multimodal systems, and agentic AI.
Pull quotes
Acronyms are not the foundation
“Mentor prompts and acronyms, in particular, act as helpful starting points, but do not guide the entire interaction.”
AI conversations as texts
“What I was really doing — though I didn’t have this language for it at the time — was treating the AI interaction as a piece of media unto itself.”
Big ideas
- AI literacy has to be taught inside real subjects
- Students need to bring the purpose; AI should not supply it for them
- Learning still needs some struggle, even when AI can make things easier
- AI tools should be judged by the work they will actually do
Claims
- Prompting AI is a literacy practice, not just a technical trick
- AI chat transcripts can make student thinking visible
- AI chat transcripts can be taught like texts
- AI literacy only works when it is connected to subject-area knowledge
Key evidence and examples
- Kentz compares prompt acronyms to sentence stems: useful starts, but not foundations for skilled communication.
- He adapts writing-class routines—comparison, annotation, discussion, voting, and rubric-building—to AI chat transcripts.
- Students identify stronger AI use by noticing specificity, context, nuance, iteration, and the quality of follow-up moves.
- The article reports versions tested across middle school, high school, college, and a Grade 12 classroom pilot.
Education relevance
Very relevant for AI literacy, writing pedagogy, and teacher professional learning because it gives educators a classroom routine for moving beyond prompt sheets toward visible, discussable AI-use practice.
Durability note
The named tools and transcript examples may evolve quickly, but the pedagogical pattern is durable: students can learn AI use by comparing, annotating, and discussing interactions as texts.