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

● high · 2026-05-26metacognition, professional-training, adaptive-learning, lifelong-learning, scaffolding, self-regulated-learning, llm

Metacognitive Learning Scenarios Taxonomy

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.

    Connected Concepts

  • Metacognition
  • Scaffolding
  • Intelligent Tutoring
  • Adaptive Learning
  • Professional Training
  • Lifelong Learning
  • Self Regulated Learning

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    • 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. arXiv:2605.24142. cs.HC.