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

Synthesis: This paper addresses a fundamental gap in Metacognition research: the lack of systematic integration of metacognitive theories into scenario taxonomies capable of guiding AI-enhanced professional development. By synthesizing four major theoretical frameworks into a six-node open systems model, the authors create a rigorous taxonomy of metacognitive learning scenarios.

Systems Model & Scenario Generation

The six-node open systems model (Environment, Input, Processes, Structures, Output, Feedback) was used for combinatorial enumeration, generating 216 mathematically possible learning scenarios. Four sequential constraint-based filters — psychological plausibility, educational relevance, measurement feasibility, and intervention potential — reduced this to 24 priority scenarios.

These 24 scenarios distribute across three developmental tiers:

• Novice (6 scenarios) — foundational metacognitive awareness
• Developing (10 scenarios) — active strategy use and monitoring
• Expert/Adaptive (8 scenarios) — flexible, context-sensitive metacognitive control

Theoretical Gaps Identified

The analysis revealed critical gaps in current metacognition theory regarding 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. These gaps have direct implications for designing Scaffolding in Intelligent Tutoring systems and Adaptive Learning platforms.

AI-Enhanced Professional Development

The taxonomy enables targeted, scenario-specific professional development interventions and generates testable predictions. It provides a structured foundation for AI systems that scaffold metacognitive growth in Workplace Learning and Lifelong Learning contexts. This complements work on Self-Regulated Learning by operationalizing the progression from novice to expert metacognitive functioning in ways that AI systems can track and support.

What this means for practice

• Designers. Place a learner in one of the three developmental tiers (novice, 6 scenarios; developing, 10; expert/adaptive, 8) and select the intervention for that tier instead of one generic metacognitive prompt.
• Designers. Track the directionality of monitoring and control in the learner model, since unidirectional processing (Processes→Structures or Structures→Processes) defines the novice tier while bidirectional integration marks the transition to developing.
• Instructors. Build professional-development activities that deliberately add external links, such as an output-to-input self-monitoring loop, so learners move from relying on performance feedback to monitoring their own work.
• Researchers. Turn the 24 priority scenarios into testable predictions and check them against validated workplace learning instruments, which the taxonomy is explicitly constrained to be measurable by.

Limitations

• The taxonomy is derived analytically: a six-node open systems model yields 216 mathematically possible scenarios, which four constraint filters reduce to 24 priority scenarios, with no learner data collected or analyzed.
• The scenario notation is ordinal. The authors note that in the fully integrated P⇄S arrangement both nodes influence each other over time, so directionality and precedence cannot be separated, which they state limits what causal modeling the representation supports.
• Priority status rests on published meta-analyses of effective metacognitive interventions and on the capabilities of existing workplace instruments rather than on measurements the authors took, so the taxonomy generates predictions it does not test.
• Scope is confined to professional learning contexts in adult learners; the implications for lifelong learning trajectories are acknowledged rather than established, and no professional-development intervention was implemented to validate the tier progression.

Citation

Gibson, D. C., Azukas, M. E., & Yilmaz Soylu, M. (2026). A taxonomy of metacognitive learning scenarios in professional contexts: Integrating systems theory with empirical constraints.