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

2026-07-30

AI Summary

This study addresses the foundational crisis in education precipitated by artificial intelligence’s capacity to efficiently generate academic outputs, thereby undermining traditional assessment paradigms that equate measurable products with human competence. The paper proposes a novel “post-instrumental learning” framework that systematically articulates the risk of “competence dissolution” induced by AI and identifies five irreducible human capacities that must remain beyond delegation: goal-setting, justification, critical questioning and revision, principled refusal, and participatory co-governance. Integrating philosophical and educational theory with institutional critique, the work develops a technology-agnostic conceptual governance framework. It contends that AI governance in education must transcend performance-based evaluation and instead prioritize sustaining human capacities to understand, contest, revise, and share responsibility for AI-mediated practices, thereby reconstituting educational and assessment paradigms for the AI era.

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.

Problem

• post-instrumental learning
• capacity dissolution
• AI-mediated assessment
• accountability
• generative AI

Innovation

• post-instrumental learning
• capacity dissolution
• generative AI
• accountable relation
• AI-mediated work