The article examines the methodological foundations of organizing students’ independent learning with generative artificial intelligence (AI) tools. It argues that AI can both strengthen independent learning and hollow it out, and proposes that its didactic value be measured not by the performance achieved together with the technology but by the cognitive residue – the knowledge, skills and thinking strategies the student retains without it. On the basis of theoretical analysis and didactic design, the study classifies six roles of AI in independent learning according to the cognitive residue they leave, develops a five-stage cycle of AI-supported independent work (independent attempt – Socratic guidance – feedback and revision – critical verification – AI-free retrieval and reflection), a five-level scale of permitted AI use in assignments and a set of six assessment criteria with indicators and weights. The discussion considers the controversy between the extended mind thesis and research on cognitive offloading. The principles of preserving desirable difficulties, gradually fading support and developing students’ metacognitive decisions are substantiated.

independent learning, generative artificial intelligence, cognitive residue, cognitive offloading, desirable difficulties, scaffolding, feedback, metacognition, assessment, higher education.

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