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.

翻译：随着人工智能模型被常规性地整合到知识工作中，认知行为日益以两种不同模式发生：一种是单独使用生物资源进行的个体模式，另一种是在人机系统间分布的分布式模式。现有对布鲁姆分类法的修订将人工智能视为需要映射到人类认知的外部能力，而非这种双模态结构的驱动因素，因此未能为每种模式明确界定具体的学习成果与评估目标。本文提出增强认知框架，这是一种基于三项原则重构的分类体系。首先，传统布鲁姆分类的每个层级均在两种模式（个体模式与分布式模式）下运作，并配备相应模式的认知动词。其次，存在一种非对称依赖关系：有效的分布式认知通常需要个体认知基础，但在某些情况下，结构化支架可以逆转这一顺序。第三，新增的第七层级——协调层，明确了管理模式切换、信任校准与伙伴关系优化的治理能力。我们依据明确的评估效用标准，系统比较了现有的人工智能修订分类法，结果表明在已审视的框架中，唯有增强认知框架能针对个体认知、分布式认知及模式治理这三个不同目标，生成可评估的学习成果。该框架通过结构性显化依赖关系，同时兼容合理的支架式教学路径，从而应对人工智能时代核心的教学风险——流利无能现象。