A Taxonomy of Metacognitive Learning Scenarios in Professional Contexts: Integrating Systems Theory with Empirical Constraints

Abstract: Metacognitive theories provide foundational frameworks for understanding self-regulated learning, yet they lack systematic integration into comprehensive scenario taxonomies capable of guiding AI-enhanced professional development interventions. Existing models inadequately specify how metacognitive components combine into distinct learning scenarios or how professionals progress from novice to expert functioning. A six-node open systems model, consisting of Environment, Input, Processes, Structures, Output, and Feedback, was developed by synthesizing four major theoretical frameworks. Combinatorial enumeration generated 216 mathematically possible learning scenarios. Four sequential constraint-based filters, including psychological plausibility, educational relevance, measurement feasibility, and intervention potential, informed by empirical workplace learning research, reduced this space to 24 priority scenarios. Five focal scenarios were subjected to formal concept analysis. The 24 priority scenarios were distributed across three developmental tiers: novice, with 6 scenarios; developing, with 10 scenarios; and expert/adaptive, with 8 scenarios. Analysis revealed critical theoretical gaps regarding 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 targeted, scenario-specific professional development interventions and generates testable predictions for advancing metacognition theory beyond primarily descriptive accounts.