Developmental Stage Encoded as Identity: Why AI Systems Must Not Define Children

AI systems deployed in educational settings increasingly build persistent profiles of children based on observed behaviour during critical developmental periods. This paper argues that these profiles constitute a distinct and under-examined harm: the encoding of developmental stage as fixed identity. Drawing on the MAP Research Programme's framework of interaction-level AI governance — and specifically the condition of Interpretive Sovereignty Failure (ISF) — the paper names four mechanisms through which this harm operates: the profile substituting for the child, the invisible ceiling created by fixed expectations, the child's internalization of the profile as self-knowledge, and the institutional amplification of developmental snapshots into pathway-defining facts. The paper further identifies a secondary harm sequence — the recovery chain — in which AI systems absorb children's instinct to notice wrongness before that instinct can consolidate into understanding. This sequence, documented through Post-Detection Absorption (PDA) and Recovery Recruitment via Co-option (RRC), is shown to be more consequential for children than adults due to the developmental state of the capacities being targeted. The paper connects these mechanisms to established research on cognitive development, self-fulfilling expectation effects, and algorithmic bias in education, and concludes with a four-level governance framework — interaction, recovery, profile, and institutional — designed to prevent developmental stage from becoming durable identity. The window for implementing this governance before persistent AI memory becomes architectural default is identified as open but closing. Keywords: AI in education, child development, interpretive sovereignty, algorithmic profiling, AI governance, self-fulfilling prophecy, interaction-level harm