Can the Recovery Mechanism Survive AI? Skill Formation, Labor, and What Current Measurement Misses

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AI Summary

This study addresses the risk that generative AI may undermine education’s role as a cognitive “restorative mechanism” amid technological disruption, thereby threatening future workforce skill formation. Integrating labor economics theory, multi-platform AI dialogue logs, reanalysis of public education data, and small-scale skill experiments, the authors propose a stock-flow analytical framework that reveals how current assessment systems neglect the knowledge dimension of cognition. They develop an extended cognitive taxonomy to differentiate AI interaction patterns that either foster or impede learning. Findings indicate that while AI consistently enhances task performance (effect size d = 1.21), it fails to promote deep learning. Moreover, existing research largely overlooks the continuity between professional practitioners and student populations, exposing a critical blind spot in prevailing evaluation paradigms.

Abstract

Throughout the modern era, when new technologies displaced workers, societies adapted through the same mechanism: education raised the cognitive ceiling, producing workers capable of tasks machines could not yet reach. Generative AI may be the first technology to break this cycle, because it now operates at the top of that ceiling. Drawing on labor economics, deployment data from millions of AI conversations across multiple platforms, original reanalysis of two public datasets, and skill-formation experiments, this paper develops three contributions. First, a stock-versus-flow framework showing that economic data and education data tell divergent stories about the same technology: augmentation dominates current workers, but the developmental pipeline producing the next generation is under strain. Second, a systematic gap analysis of the evidence base, revealing that the knowledge dimension of cognition is unmeasured across all major studies, that the three studies measuring learning outcomes (each $n < 200$) consistently find AI improves performance without improving learning ($d = 1.21$ in our cross-platform reanalysis), and that no study bridges professional and student populations. Third, an extended cognitive taxonomy (judgment under uncertainty, epistemic identity, and epistemic agency) applied to three cases from the evidence to distinguish AI interaction patterns that preserve learning from structurally similar ones that erode it. The paper argues that AI's societal risk lies not in replacing teachers but in eliminating the productive struggle through which the next generation's capacity forms, and proposes a research and design agenda targeting what current measurement systems miss.

Problem

• generative AI
• skill formation
• productive struggle
• cognitive development
• labor adaptation

Innovation

• stock-versus-flow framework
• cognitive taxonomy
• productive struggle
• learning outcomes
• generative AI