Date
Publisher
arXiv
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.
Study at a glance
- Question: How can human epistemic agency be preserved when generative capacity is increasingly externalized to artificial agents?
- Setting and data: Not stated in the paper
- Method: Conceptual framework synthesis integrating distributed cognition, cognitive load theory, and self-regulated learning
Why it matters
Generative AI can boost immediate performance while eroding learners' internal monitoring, calibration, and cognitive engagement, threatening their capacity to independently generate and evaluate knowledge claims.
Key findings
- Hybrid cognition can become destabilized through seven interacting mechanisms, including control redistribution, germane effort attenuation, calibration drift, delegation drift, representational passivity, dual-threshold instability, and motivational-affective drift.
- Five regulatory operators—Anticipate, Interrogate, Reflect, Integrate, and Synthesize—can counteract cognitive drift by targeting internal generative engagement at points of emerging instability.
- Dual-threshold instability causes disproportionate, nonlinear performance degradation specifically when both internal human generative engagement and generative AI reliability simultaneously fall below task demands.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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