发表机构
Ludwig-Maximilians-Universität München; Leibniz-Insitut für Wissensmedien; University of Tübingen; The University of North Carolina at Chapel Hill; Saarland University; Heidelberg University of Education（慕尼黑大学; 莱布尼茨知识媒体研究所; 蒂宾根大学; 北卡罗来纳大学教堂山分校; 萨尔兰大学; 海德堡教育大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出AIRIS框架，分析人机混合认知的元认知困境，明确保留认知能动性的条件，提供相关研究与设计工具包。
AI 中文摘要
生成式人工智能（genAI）系统正日益成为假设生成、解释构建和决策等认知过程的核心组成部分。尽管它们能可靠地提升性能，但越来越多的证据揭示了一种元认知困境：随着外部生成能力的增强，内部监测、校准和认知参与可能会下降。这反映了分布式人机系统中认知控制的重新分配，仅用自动化偏差或对算法的依赖无法对此作出解释。我们提出AIRIS（AI-Augmented Inquiry and Regulation in Hybrid Systems，混合系统中AI增强式探究与调控）框架，用于分析该困境并明确可抵消困境的调控干预点。AIRIS是一种多级控制分配架构，明确了在混合生成系统中保留认知能动性的条件。它借鉴分布式认知、认知负荷理论、多媒体学习和自我调节学习理论，识别出混合认知可能不稳定的七种相互作用机制，从委派、校准漂移到动机-情感漂移。五种调控算子（Anticipate、Interrogate、Reflect、Integrate和Synthesize）在不稳定出现时针对内部生成参与进行调控。该架构本身不提升学习，它明确了genAI支持的工作若要维持理解必须保留的要素，无论是通过教学设计、教师指导还是学习者自身的调控。我们推导了关于七种机制和五种算子的可检验命题，将AI增强重新定义为分布式生成系统中的控制分配问题。除理论外，AIRIS还提供了研究议程、生成式AI整合学习环境的设计框架，以及人机混合认知治理的概念工具包。
英文摘要
Generative artificial intelligence (genAI) systems are increasingly integral to epistemic processes such as hypothesis generation, explanation construction, and decision-making. Although they reliably enhance performance, emerging evidence reveals a metacognitive dilemma: as external generative capacity increases, internal monitoring, calibration, and cognitive engagement may decline. This reflects a redistribution of cognitive control within distributed human-AI systems that cannot be explained by automation bias or reliance on algorithms alone. We propose the AIRIS (AI-Augmented Inquiry and Regulation in Hybrid Systems) framework to analyze this dilemma and specify where regulatory intervention can counteract it. AIRIS is a multi-level control allocation architecture specifying the conditions under which epistemic agency can be preserved in hybrid generative systems. Drawing on distributed cognition, cognitive load theory, multimedia learning, and self-regulated learning, it identifies seven interacting mechanisms through which hybrid cognition may become destabilized, from delegation and calibration drift to motivational-affective drift. Five regulatory operators (Anticipate, Interrogate, Reflect, Integrate, and Synthesize) target internal generative engagement at points of emerging instability. The architecture does not itself improve learning; it specifies what must remain in place for genAI-supported work to sustain understanding, whether through instructional design, teacher guidance, or learners' own regulation. We derive testable propositions concerning the seven mechanisms and the five operators, reframing AI augmentation as a problem of control allocation in distributed generative systems. Beyond theory, AIRIS offers a research agenda, a design framework for genAI-integrated learning environments, and a conceptual toolkit for the governance of hybrid human-AI cognition.
