Toward a Metric for Disciplinary Learning in the Age of AI: The UnBlooms™ Metacognitive Awareness Scale and Discernment Rate in AI-Mediated Learning

Telling students to “reflect on their own thinking” has become insufficient in AI-mediated learning environments. When students are rewarded for polished outputs, cognitive offloading to AI tools becomes rational, and traditional snapshot assessments (single-moment evaluations of task completion) produce false signals about whether durable learning has occurred. This problem is sharpened by the broader shift in AI learning tools toward Socratic tutors, study modes, Khan Academy's Khanmigo, and agentic systems that can shape the learner’s process over time. Lodge and Loble (2026) distinguish beneficial cognitive offloading, which frees working memory for intrinsic learning, from detrimental outsourcing, which bypasses the cognitive work that builds durable understanding. This paper introduces two behavioral instruments designed to make that distinction observable in classroom contexts. The UnBlooms™ Metacognitive Awareness Scale (MAS) is a five-level developmental taxonomy that operationalizes evaluative judgment as instructor-scored evidence. The UnBlooms™ Discernment Rate (UDR) is a classroom-level metric tracking the proportion of AI outputs a learner interrogates, challenges, or revises rather than accepting at face value. Together, the MAS and UDR shift assessment from product snapshots toward longitudinal interaction trajectories: the sequence of decisions, revisions, and resistances through which metacognitive development becomes visible. The paper situates these tools in the cognitive offloading literature, compares them to the recently validated Metacognitive Laziness Scale (Dizon et al., 2026), and proposes testable hypotheses linking MAS, UDR, and metacognitive laziness. It also identifies the validation work required before these instruments can be treated as psychometrically established measures.