The Long Game: Why AI Implementation Is a 3–5 Year Rebuild
Source: Nick Potkalitsky Substack
Author: Nick Potkalitsky
Original source: https://nickpotkalitsky.substack.com/p/the-long-game-why-ai-implementation
Published: 2025-12-14
Source type: essay
Private backup: the full article text is archived in the private repository at archives/articles/nickpotkalitsky-substack-com-the-long-game-why-ai-implementation.source.md. It is not published on the public Quartz site.
Summary
Nick Potkalitsky argues that K–12 AI implementation should be treated as a multi-year institutional rebuild rather than a quick compliance exercise or tool-adoption push. Drawing on a presentation to the Ohio 8 Coalition, he frames Ohio’s district AI policy deadline as an opportunity to build comprehensive guidance around instruction, assessment, technology ecosystems, data governance, equity, wellbeing, and teacher-led curriculum redesign.
Pull quotes
A multi-year rebuild
“We are in the middle of a 3-5 year rebuild.”
Homework has changed
“Any work sent outside of class will be completed with the assistance of AI.”
Teachers at the center
“Because teachers continue to be the superpower during this AI disruption.”
More intentional implementation
“The solution isn’t less AI. It’s more intentional implementation.”
Durability note
The implementation timeline and state-policy context are time-bound, but the central lesson is durable: AI adoption becomes educationally meaningful only when schools redesign guidance, assessment, teacher learning, and curriculum over time.
Big ideas
Claims
- District AI implementation needs living guidance and teacher-led redesign
- AI-assisted homework requires redesign, not just policing
- Rushed school AI plans can worsen wellbeing and equity risks
Key evidence and examples
- Ohio House Bill 96 requires districts to adopt AI policies, creating a policy deadline that could become more than compliance.
- Potkalitsky describes many districts as reactive and compliance-focused, with limited instructional guidance.
- He argues that out-of-class work should be assumed to involve AI assistance, which destabilizes traditional homework and assessment assumptions.
- He proposes instructional redesign, assessment redesign, purposive technology ecosystems, and teacher-led curriculum rebuilding.
- He cites concerns about student emotional dependence, disconnection from teachers, AI use for mental health support, deepfakes, and unequal harms to vulnerable students.
Education relevance
This article is highly relevant to K–12 district AI strategy, policy implementation, curriculum redesign, assessment, procurement, data governance, teacher professional learning, and student wellbeing.