Generative AI and the Purpose of Schooling: A Theoretical Framework of Learning, Offloading and the Competencies That Remain Human
DOI:
https://doi.org/10.68854/kxj3rz10Keywords:
: generative AI, curriculum, cognitive offloading, learning theory, competencies, critical thinking, assessment, educational philosophy, basic educationAbstract
When a system can draft the essay, solve the problem set and explain the concept, the question for education is no longer how to stop students using it but what schooling is for. Debate has been dominated by academic integrity and short-term performance gains, and it lacks a theory explaining why the same tool can raise assisted performance while weakening unaided learning. The objective of this conceptual paper is to develop such a theory and derive testable propositions for curriculum, assessment and classroom practice. The population is the peer-reviewed literature on generative AI in education, the science of learning and cognitive offloading, from which we drew a sample of 33 journal articles: 31 published 2023–2026 and two foundational works cited for the theories they originated. Sources were selected by criterion-based (purposive) sampling, and every reference was verified against Crossref and screened for retractions. The analysis was a conceptual and thematic synthesis: studies were coded for what learner and system did and when learning was measured, the codes were read against learning mechanisms, and propositions were derived where theory and evidence converged. We distinguish three relations a learner can have with a generative system: learning with it, learning from it and offloading to it. Offloading, the default under general-purpose designs, bypasses the processes that build the knowledge and judgement on which the other two relations depend. Four competencies therefore become more rather than less important: evaluative judgement, domain knowledge sufficient to detect error, sustained unaided reasoning and the disposition to verify. Ten falsifiable propositions follow, among them unaided-first sequencing, validity-centred assessment and tool configurations that withhold answers. For African basic education systems, where generative AI is reaching learners faster than teacher preparation, we recommend protecting foundational learning, building teachers' evaluative capacity rather than tool fluency, treating supervised unaided assessment as an asset, and reducing the workload and time pressure that may push pupils towards offloading. The purpose of schooling under generative AI, we conclude, is to produce people who can tell when the machine is wrong.
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