Metacognition as Disciplinary Infrastructure in AI-Mediated Learning

Authors

• Tina R. Austin, UCLAFollow
• Jason Gulya, BerkelyFollow
• NICK Potkalitsky, Educational Service Center of Central OhioFollow

Research Highlights

• The Problem: Generative artificial intelligence has disrupted traditional academic instruction and assessment by redistributing cognitive labor, rendering generalized metacognitive frameworks insufficient to guide student reasoning or prevent the substitutive effects of tool reliance.

• The Method: The researchers conceptualize an integrated pedagogical approach combining Tina Austin’s UnBlooms Framework, the UnBlooms Discernment Spiral, the Metacognitive Awareness Scale, and Jason Gulya’s Pre-Creation Loop to embed discipline-specific metacognitive checkpoints into student workflows.

• Quantitative Finding: The UnBlooms Metacognitive Awareness Scale consists of 5 distinct levels; empirical data regarding assessment redesigns was collected from educators and presented on April 10, 2026.

• Qualitative Finding: Effective metacognition must be anchored within a discipline-responsive infrastructure rather than serving as a generic, terminal reflection; artificial intelligence-mediated learning necessitates dual-system vigilance to separate human reasoning from fluent machine output; intentional pedagogical friction is required to preserve student authorship, intellectual ownership, and human agency.

Abstract

Metacognition is often presented as a response to generative AI’s disruption of teaching and learning, yet the term has become too generalized to guide practice. In AI-mediated environments, asking students merely to “reflect on your thinking” is insufficient. Because AI tools can redistribute cognitive labor, their educational value depends on how students use them and whether that use supports disciplinary forms of reasoning. This essay argues that metacognition must be understood as disciplinary infrastructure: students cannot effectively monitor their thinking without understanding the epistemological and ontological demands of the field in which they are working. Classrooms therefore become sites where student frameworks interact with more discipline-grounded instructional frameworks. Teachers must balance immersion and friction, enabling students to enter the flow of inquiry while introducing strategic pauses that make disciplinary expectations visible. Tina Austin’s UnBlooms Framework offers one model through “metacognitive checkpoints,” where students evaluate whether AI is helping or hindering their learning. These checkpoints ask students to discern when AI supports a discipline-responsive habit of mind and when they should resist offloading and complete a task themselves. The essay reframes metacognition as concrete, discipline-sensitive, and grounded in judgment within AI-mediated learning.

A link to a video related to this presentation can be found below in the Additional Files section.

Recommended Citation

Austin, Tina R.; Gulya, Jason; and Potkalitsky, NICK (2026) "Metacognition as Disciplinary Infrastructure in AI-Mediated Learning," International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM: Vol. 4: Iss. 1, Article 6.
DOI: https://doi.org/10.62608/2831-3550.1055
Available at: https://digitalcommons.lindenwood.edu/ijedie/vol4/iss1/6