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

Kai Yao (2026) — arXiv:2607.28041 (cs.CY)

📄 Full text (arXiv)

Summary

Argues that as AI systems become capable of producing the artifacts through which institutions recognize competence, existing ethical frameworks centered on AI failures become insufficient. Develops the concept of "post-instrumental learning" and warns that each technical improvement appears to weaken the case for human learning itself, risking "capacity dissolution."

The work connects to broader discussions in AI and education around over-reliance, learning-outcomes, ai-literacy, contributing to our understanding of how generative ai shapes educational practice.

Key Contributions

• Contributes empirical or theoretical advances relevant to the over-reliance domain

• Published in 2026, reflecting the fast-moving landscape of AI in education research

• Engages with questions of educational theory and over reliance in educational contexts

Related Pages

• over-reliance

• learning-outcomes

• ai-literacy

• critical-thinking

• educational-theory

Citation

APA: Kai Yao (2026). When AI Does the Work, What Is Learning For? Post-Instrumental Learning and the Risk of Capacity Dissolution. arXiv:2607.28041. cs.CY.