Revising Bloom's Taxonomy for Dual-Mode Cognition in Human-AI Systems: The Augmented Cognition Framework

This paper proposes the Augmented Cognition Framework (ACF), a revised version of Bloom's Taxonomy that distinguishes between Individual and Distributed cognitive modes (human alone vs. human-AI collaboration), adds a seventh 'Orchestration' level for managing mode-switching, and addresses the pedagogical risk of 'fluent incompetence' where students rely on AI without developing foundational cognitive skills.

As artificial intelligence (AI) models become routinely integrated into knowledge work, cognitive acts increasingly occur in two distinct modes: individually, using biological resources alone, or distributed across a human-AI system. Existing revisions to Bloom's Taxonomy treat AI as an external capability to be mapped against human cognition rather than as a driver of this dual-mode structure, and thus fail to specify distinct learning outcomes and assessment targets for each mode. This paper p

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