When AI Does the Work, What Is Learning For? Post-Instrumental Learning and the Risk of Capacity Dissolution

• URL: http://arxiv.org/abs/2607.28041v1
• Date: Thu, 30 Jul 2026 11:24:56 GMT
• Title: When AI Does the Work, What Is Learning For? Post-Instrumental Learning and the Risk of Capacity Dissolution
• Abstract summary: Existing AI ethics rightly emphasizes present failures.<n>But if the case for learning rests only on those failures, then each technical improvement appears to weaken it.<n>Using the idealization of AI that executes specified tasks flawlessly while lacking authority over purposes, legitimacy, and responsibility, we argue for post-instrumental learning.
• Score: 3.2940515388832963
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As AI systems become capable of producing the essays, code, reports, summaries, plans, and decisions through which institutions usually recognize competence, a familiar question becomes harder to answer: what is learning for? Existing AI ethics rightly emphasizes present failures--bias, opacity, hallucination, labor extraction, privacy risk, and weak accountability. But if the case for learning rests only on those failures, then each technical improvement appears to weaken it. This article develops a different answer. Using the idealization of AI that executes specified tasks flawlessly while lacking authority over purposes, legitimacy, and responsibility, we argue for post-instrumental learning: learning that preserves the capacities people and institutions need when many useful outputs can be delegated. We analyze five such capacities--end-setting, reason-giving, contestability, refusal/revision, and participation--and name their erosion capacity dissolution. The central case is assessment under generative AI. When a polished artifact no longer reliably evidences understanding, institutions must assess the learner's accountable relation to AI-mediated work rather than the artifact alone. The takeaway is practical: AI governance should evaluate not only whether systems perform well, but also whether their deployment leaves people able to understand, challenge, revise, and share responsibility for the practices those systems mediate.

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