Training for Obsolescence? The AI-Driven Education Trap

TL;DR

This paper presents a theoretical model showing that AI-driven education can lead to skill mismatches and overinvestment in obsolescent skills due to information frictions and neglect of non-cognitive skills.

Contribution

It introduces a model analyzing how AI's immediate productivity benefits and future wage effects cause systematic skill mismatches in education planning.

Findings

Overinvestment in skills prone to obsolescence increases with AI prevalence.

Neglect of non-cognitive skills worsens skill mismatch.

Endogenous AI adoption amplifies the mismatch.

Abstract

Artificial intelligence is simultaneously transforming the production function of human capital in schools and the return to skills in the labor market. We develop a theoretical model to analyze the potential for misallocation when these two forces are considered in isolation. We study an educational planner who observes AI's immediate productivity benefits in teaching specific skills but fails to fully internalize the technology's future wage-suppressing effects on those same skills. Motivated by a pre-registered pilot study suggesting a positive correlation between a skill's "teachability" by AI and its vulnerability to automation, we show that this information friction leads to a systematic skill mismatch. The planner over-invests in skills destined for obsolescence, a distortion that increases monotonically with AI prevalence. Extensions demonstrate that this mismatch is exacerbated by the neglect of unpriced non-cognitive skills and by the endogenous over-adoption of educational technology. 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, such as by crowding out skills like persistence that are forged through intellectual struggle.