Metacognitive Monitoring in AI Contexts

What This Skill Does

Analyses how AI tool use in a specific learning context might distort students' metacognitive monitoring, their ability to accurately assess what they know and don't know, and designs interventions to maintain metacognitive accuracy. This is one of the most urgent challenges in AI-enabled education. When a student uses an AI tool to complete work, they may experience a fluency illusion: the work looks good, the answers are correct, the text is fluent, and the student concludes "I understand this." But the STUDENT didn't do the cognitive work; the AI did. The student's sense of understanding is calibrated to the PRODUCT (which is good) rather than to their OWN knowledge (which may be unchanged). Bjork et al. (2013) showed that learners are systematically poor at judging their own learning, they confuse familiarity with understanding, and fluent performance with durable knowledge. AI tools dramatically amplify this miscalibration because they produce fluent, correct output that the student may mistake for evidence of their own competence. The output includes a metacognitive diagnosis (how AI use distorts self-assessment in this specific context), monitoring interventions (strategies to improve metacognitive accuracy), AI usage guidelines (when to use and when to restrict AI), and assessment alignment (ensuring tests measure student knowledge, not AI-assisted performance).

Evidence Foundation