Are We Pathologizing AI Use Too Quickly?
Source: Educating AI / Nick Potkalitsky Substack
Author: Nick Potkalitsky
Original source: https://nickpotkalitsky.substack.com/p/are-we-pathologizing-ai-use-too-quickly
Private backup: the full article text is archived in the private repository at archives/articles/nickpotkalitsky-substack-com-are-we-pathologizing-ai-use-too-quickly.source.md. It is not published on the public Quartz site.
Summary
Nick Potkalitsky cautions against prematurely describing AI use through clinical labels such as “AI addiction,” “AI psychosis,” or emotional dependence, especially when those terms migrate into school policy before the research stabilizes. He acknowledges that some people are genuinely harmed by AI interactions and may need clinical support, but argues that education systems should distinguish clinical risk, problematic use, and normal high use. Drawing parallels to earlier debates over internet and smartphone addiction, he argues that specificity and functional impairment matter more than broad moral panic. For K-12 schools, he recommends harm reduction, AI literacy, targeted safeguards, and referral pathways rather than blanket bans or punitive enforcement.
Big ideas
- District AI work is a long-term redesign project
- AI simulations need clear boundaries for learning
- Schools should start with learning values before choosing AI tools
Claims
- Rushed school AI plans can worsen wellbeing and equity risks
- Treating normal AI use as pathology can lead to worse school policy
Key evidence and examples
- The article discusses clinical claims around “AI psychosis” as possible amplification of existing distress rather than a simple new diagnosis.
- Research on problematic AI chatbot use is described as involving loneliness, social anxiety, emotional reliance, escapism, and immersive flow.
- Earlier internet and smartphone addiction debates are used to argue for specificity and functional impairment.
- Potkalitsky separates clinical risk, problematic use, and normal high use as different policy categories.
Education relevance
Highly relevant for K-12 AI policy, student wellbeing, counselor referral pathways, harm reduction, and district-level implementation.