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Jeffrey A. Greene

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Preprint Aug 2026

AI-Augmented Inquiry and Regulation in Hybrid Systems: A Control Allocation Architecture for Preserving Epistemic Agency in Hybrid Human-AI Cognition

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.

Jochen Kuhn, P. Gerjets, Ulrich Trautwein et al. · 0 citations
Book Jul 2026

A Qualitative Examination of Undergraduate CS Students' Self-Regulated Learning

Self-regulated learning (SRL) is an instrumental skill for success in learning computer science (CS) and software engineering. SRL is an active process where students engage in cycles of planning, strategy-use, monitoring and control, and adaptation to accomplish goals. There have been calls to investigate whether SRL theory and measurement need to be made more specific to the CS education context and to integrate SRL theory when interpreting observations of student behavior in computing education research. In this work we examine the self-regulatory behavior of CS students in a 200-level CS course with an emphasis on software engineering. We performed a think-aloud study with thirty-three students working on their programming projects. Researchers then coded the think aloud data using a theory-driven codebook aligning with Winne and Hadwin's COPES model of SRL. Using the coded data we performed a thematic analysis of two codes to better understand how students monitor their understanding of the task and set goals. Our results revealed a more complex picture than prior work of how students monitor their understanding of the task and set subgoals. We discuss the implications of our results for CS instruction and intervention designs to promote more effective SRL and learning.

J. Bacher, Christina L. Hollander, Michael Berro et al. · 0 citations