Thinking with AI: The Teacher Workshop

Source: Nick Potkalitsky Substack
Author: Nick Potkalitsky
Original source: https://nickpotkalitsky.substack.com/p/thinking-with-ai-the-teacher-workshop Source type: essay

Private backup: the full article text is archived in the private repository at archives/articles/nickpotkalitsky-substack-com-thinking-with-ai-the-teacher-workshop.source.md. It is not published on the public Quartz site.

Summary

Potkalitsky introduces teacher-facing workshop materials for disciplinary AI literacy: a 1.5-hour introductory workshop, a 3-hour deep dive, and six cross-disciplinary sample lessons for grades 6–12. The core design principle is that AI should not do the thinking for students. Students should first write, read, frame, argue, or otherwise commit to their own thinking before AI enters the activity. Each lesson ends with reflection on what students accepted from AI, what they rejected, and why.

Pull quotes

The central workshop question

“how do you structure learning so that the thinking lands with students, not with AI?”

Cross-disciplinary by design

“Both workshops are built for cross-disciplinary audiences, because the conversation across subject areas is not a compromise. It is the point.”

Before AI enters

“Every one of them begins before AI enters the room.”

Protecting cognitive work

“It asks them to do what they have always done: design for student thinking, assess student moves, and protect the cognitive work that makes learning real.”

Durability note

The linked workshop resources may change over time, but the design principle is durable: teachers need AI literacy activities that protect student thinking before, during, and after AI use.

Big ideas

Claims

Key evidence and examples

  • The workshop formats include “Same Technology. Different Trajectories” and “Designing for Student Thinking in an AI-Rich World.”
  • The resource suite includes six sample lessons across ELA, social studies, science, and math for grades 6–12.
  • A repeated sequence asks students to think, write, read, frame, or argue before AI enters.
  • Final reflection asks students what they accepted from AI, what they rejected, and why.

Education relevance

High relevance for K–12 AI professional learning, disciplinary AI literacy, curriculum design, visible student cognition, and teacher-led lesson implementation.

My notes