Gibson, Azukas & Yilmaz Soylu (2026) — Curtin University, East Stroudsburg University.
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 professional-training 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.Related Pages
- metacognition — Metacognition in learning
- self-regulated-learning — Self-regulated learning
- intelligent-tutoring — Intelligent tutoring systems
- adaptive-learning — Adaptive learning systems
- professional-training — Professional training and AI
- scaffolding — Instructional scaffolding
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