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

Abstract

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 proposes the Augmented Cognition Framework (ACF), a restructured taxonomy built on three principles. First, each traditional Bloom level operates in two modes (Individual and Distributed) with mode-specific cognitive verbs. Second, an asymmetric dependency relationship holds wherein effective Distributed cognition typically requires Individual cognitive foundations, though structured scaffolding can in some cases reverse this sequence. Third, a seventh level, Orchestration, specifies a governance capacity for managing mode-switching, trust calibration, and partnership optimization. We systematically compare existing AI-revised taxonomies against explicit assessment-utility criteria and show, across the frameworks reviewed, that ACF uniquely generates assessable learning outcomes for individual cognition, distributed cognition, and mode-governance as distinct targets. The framework addresses fluent incompetence, the central pedagogical risk of the AI era, by making the dependency relationship structurally explicit while accommodating legitimate scaffolding approaches.