Epistemic dependence in AI-mediated learning

Abstract

Artificial intelligence (AI) systems increasingly mediate how learners obtain explanations, synthesise sources, receive feedback and judge the quality of academic work. This critical-integrative review argues that the central educational question is not whether learners rely on AI, but whether that reliance preserves or displaces the epistemic work through which judgement develops. The review purposively bridges artificial intelligence in education, human–AI interaction, epistemic cognition, information behaviour, cognitive offloading, and social epistemology, while engaging a recent literature on epistemic authority, agency, infrastructure and over-reliance. It conceptualises epistemic dependence as reliance on AI for knowledge-related tasks and differentiates productive reliance from harmful dependence through six diagnostic criteria: contestability, recoverability, transfer, traceability, distributed responsibility and epistemic plurality. Four sociotechnical pathways are then analysed: fluent authority, frictionless delegation, opaque synthesis and institutionalised dependence. These pathways may affect verification, evaluative and disciplinary judgement, understanding, uncertainty tolerance, learner identity and epistemic justice, but the review does not assume that AI uniquely or uniformly causes these outcomes. It proposes relational epistemic agency as the normative aim: the capacity to question, verify, compare, justify and take responsibility for knowledge claims within human, technological and institutional relations. Design, pedagogical, assessment and governance implications are developed accordingly.