What Happened When We Taught AI Literacy Like Writing
Source: Mike Kentz Substack
Author: Aimée Skidmore, Mike Kentz
Original source: https://mikekentz.substack.com/p/what-happened-when-we-taught-ai-literacy
Published: 2026-02-08
Source type: essay
Private backup: the full article text is archived in the private repository at archives/articles/mikekentz-substack-com-what-happened-when-we-taught-ai-literacy.source.md. It is not published on the public Quartz site.
Summary
Aimée Skidmore reports a four-week classroom pilot using comparative transcript analysis with 21 Grade 12 Media and Communications students at Collège du Léman. Instead of evaluating only AI outputs, students examined chat transcripts as visible evidence of strategy, reasoning, questioning, and ownership of thought. Students compared examples, co-created criteria, produced annotated transcripts, and reflected on their own AI use. The pilot was promising but preliminary, with limitations including small sample size, self-report data, short duration, task-design issues, and an initially over-fixed rubric.
Pull quotes
Replacing struggle
“AI wasn’t replacing student work. It was replacing student struggle.”
Visible and assessable
“Together, these shifts showed that when AI interaction itself became visible and assessable, students not only changed their behavior but also recognized its value for their learning.”
Intentional relationship
“When we make their interactions visible and assessable, we give them a chance to build an intentional relationship with the tools they are already using.”
Big ideas
- AI literacy has to be taught inside real subjects
- 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 tools should be judged by the work they will actually do
Claims
- AI chat transcripts can make student thinking visible
- AI chat transcripts can be taught like texts
- Prompting AI is a literacy practice, not just a technical trick
- AI can undermine learning when students use it without guidance
- Failed AI pilots are useful evidence
Key evidence and examples
- The class included 21 Grade 12 students from 14 countries, many multilingual learners and some students with identified learning needs.
- Reported outcomes included 85.7% of students changing their AI approach, 47.6% becoming significantly more strategic, and 81% endorsing continued use of the method.
- Students practiced giving rationale, asking “why” questions, annotating transcripts, and reflecting on prompt choices.
- Skidmore reports design limits: the rubric was too fixed, the initial task was too simple, and some students still shifted hard thinking onto the tool.
Education relevance
Extremely relevant for classroom AI literacy because it provides a concrete teacher-led example of making AI use visible, discussable, and assessable without pretending the evidence is stronger than a small pilot supports.
Durability note
The classroom results are an early small-scale pilot rather than settled evidence, but the durable contribution is the design pattern: make AI interaction visible, assessable, and reflective.