AI-Ready Taxonomy Maps 24 Metacognitive Learning Scenarios for Professionals

Researchers David C. Gibson, Mary Elizabeth Azukas, and Meryem Yilmaz Soylu present a new taxonomy of metacognitive learning scenarios tailored for professional contexts. Their six-node open systems model—comprising Environment, Input, Processes, Structures, Output, and Feedback—synthesizes four major theoretical frameworks. Combinatorial enumeration initially produced 216 mathematically possible learning scenarios. The team then applied four sequential constraint-based filters informed by empirical workplace learning research: psychological plausibility, educational relevance, measurement feasibility, and intervention potential. This filtering reduced the space to 24 priority scenarios, with five focal scenarios subjected to formal concept analysis.

The 24 priority scenarios distribute across three developmental tiers: novice (6 scenarios), developing (10 scenarios), and expert/adaptive (8 scenarios). Analysis revealed critical theoretical gaps, including the dynamic reconfiguration of monitoring-control relationships across expertise levels, the role of feedback topology in metacognitive development, and trade-offs between internal integration and external connectivity. Multiple viable developmental trajectories were identified. The taxonomy enables scenario-specific professional development interventions and generates testable predictions for advancing metacognition theory beyond descriptive accounts. This work directly supports designing AI systems that adapt training to individual learners' metacognitive stages.