Educational AI Auditing Across the Learning Lifespan: Concepts, Methods, Evidence, and Stage-Sensitive Governance

Educational AI auditing is the systematic, evidence-based examination of artificial intelligence systems used in formal, non-formal, and informal education to determine whether they are technically reliable, pedagogically valid, developmentally appropriate, equitable, accessible, safe, and institutionally accountable. Its object is not only a model but the situated arrangement through which models, data, interfaces, people, policies, and organizational routines redistribute educational opportunities, judgments, labor, and risk. Unlike benchmarking, an audit does not stop at task performance; unlike prospective impact assessment, it tests claims with empirical evidence; and unlike compliance review, it can ask whether a lawful use is educationally defensible. A stage-sensitive audit asks what educational work is delegated to AI, whether that delegation expands or displaces learners’ and educators’ capabilities, how effects differ across contexts and social positions, and who can contest or remedy harmful outcomes. The framework presented here combines four system levels, seven audit domains, five lifecycle gates, and a burden of evidence that rises with decision stakes, opacity, developmental dependence, and the persistence or irreversibility of consequences.