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Training for Obsolescence? The AI-Driven Education Trap

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  • Andrew J. Peterson

Abstract

Artificial intelligence simultaneously transforms human capital production in schools and its demand in labor markets. Analyzing these effects in isolation can lead to a significant misallocation of educational resources. We model an educational planner whose decision to adopt AI is driven by its teaching productivity, failing to internalize AI's future wage-suppressing effect on those same skills. Our core assumption, motivated by a pilot survey, is that there is a positive correlation between these two effects. This drives our central proposition: this information failure creates a skill mismatch that monotonically increases with AI prevalence. Extensions show the mismatch is exacerbated by the neglect of unpriced non-cognitive skills and by a school's endogenous over-investment in AI. Our findings caution that policies promoting AI in education, if not paired with forward-looking labor market signals, may paradoxically undermine students' long-term human capital, especially if reliance on AI crowds out the development of unpriced non-cognitive skills, such as persistence, that are forged through intellectual struggle.

Suggested Citation

  • Andrew J. Peterson, 2025. "Training for Obsolescence? The AI-Driven Education Trap," Papers 2508.19625, arXiv.org.
    • Handle: RePEc:arx:papers:2508.19625
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