Date
Publisher
arXiv
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 human cognition, behavior, motivation, and social interaction in ways that cannot be fully specified, predicted, or exhaustively modeled. Tasks that may appear separable in principle derive their instructional value in practice from ongoing contextual interpretation across learners, situations, and relationships. As long as educational practice relies on emergent understanding of human cognition and learning, teaching remains a form of professional work that resists automation. AI may improve access to information and support selected instructional activities, but it does not remove the need for human judgment and relational accountability that effective teaching requires.
Study at a glance
- Question: Can teaching be cleanly divided into modular tasks that artificial intelligence can take over?
- Setting and data: Not stated in the paper
- Method: Conceptual analysis integrating empirical literature on large language models and situated learning theory
Why it matters
Claims that artificial intelligence can automate teaching treat instructional work as modular and procedural. Clarifying the limits of automation is increasingly urgent as AI tools rapidly enter classrooms with limited pedagogical consideration.
Key findings
- Teaching resists automation because instructional tasks are inherently non-modular and derive their value from interconnected professional judgment across learners, time, and pedagogical contexts.
- Large language models consistently struggle with interpretive judgment and contextual reasoning, often favoring literalism and surface-level correctness over deeper pedagogical understanding.
- Instruction engages with partially observable internal human states—including motivation, affect, and trust—that cannot be fully formalized or solved through computational response generation.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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Study design
