Why teaching resists automation in an AI-inundated era: Human judgment, non-modular work, and the limits of delegation
Debates about artificial intelligence (AI) in education often portray teaching as a modular and procedural job that can increasingly be automated or delegated to technology. This brief communication paper argues that such claims depend on treating teaching as more separable than it is in practice. Drawing on recent literature and empirical studies of large language models and retrieval-augmented generation systems, I argue that although AI can support some bounded functions, instructional work remains difficult to automate in meaningful ways because it is inherently interpretive, relational, and grounded in professional judgment. More fundamentally, teaching and learning are shaped by hum...
| Authors | Songhee Han |
|---|---|
| Primary category | cs.CL |
| Published | 8 April 2026 |
| Last updated at source | 3 July 2026 |
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