修订布卢姆分类法以适应人机系统双模式认知：增强认知框架
Revising Bloom's Taxonomy for Dual-Mode Cognition in Human-AI Systems: The Augmented Cognition Framework
AI总结：
本文提出增强认知框架，通过重构布卢姆分类法，解决人机系统中双模式认知的评估问题，明确个体与分布认知及模式治理的学习成果。
摘要：
随着人工智能模型被常规整合到知识工作中，认知行为越来越多地在两种不同的模式中发生：单独使用生物资源，或在人机系统中分布。现有对布卢姆分类法的修订将AI视为一种外部能力，将其映射到人类认知，而不是作为这种双模式结构的驱动因素，因此未能指定每种模式下的不同学习结果和评估目标。本文提出了增强认知框架（ACF），一种基于三个原则重构的分类法。首先，每个传统的布卢姆等级在两种模式（个体和分布）中运作，具有特定模式的认知动词。其次，存在不对称的依赖关系，其中有效的分布认知通常需要个体认知基础，尽管结构化的支架在某些情况下可以逆转这种顺序。第三，第七级“协调”指定了管理模式切换、信任校准和伙伴关系优化的治理能力。我们系统地比较了现有AI修订的分类法与显式的评估实用性标准，并展示，在所审查的框架中，ACF唯一地为个体认知、分布认知和模式治理生成可评估的学习结果作为不同的目标。该框架通过使依赖关系结构上明确，同时容纳合法的支架方法，解决了流畅无能，即人工智能时代的核心教学风险。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
