Metacognition as Disciplinary Infrastructure in AI-Mediated Learning

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.