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.

翻译：元认知理论为理解自我调节学习提供了基础性框架，但尚未系统性地整合进能够指导人工智能增强型专业发展干预的综合性场景分类体系中。现有模型未能充分说明元认知要素如何组合形成不同的学习场景，也未阐明专业人员如何从新手发展为专家水平。本研究通过综合四大理论框架，构建了一个六节点开放系统模型，包含环境、输入、过程、结构、输出和反馈六个节点。通过组合枚举方法，生成了216种数学上可能的学习场景。基于经验性职场学习研究，依次施加了心理学可行性、教育相关性、测量可操作性及干预潜力四个约束性过滤条件，将场景空间缩减至24个优先场景。其中五个焦点场景接受了形式概念分析。24个优先场景分布于三个发展层级：新手层级（6个场景）、发展层级（10个场景）及专家/适应层级（8个场景）。分析揭示了关键理论空白：监控-控制关系在不同专长水平上的动态重构问题、反馈拓扑结构在元认知发展中的作用机制，以及内部整合与外部连接之间的权衡关系。研究识别出多条可行的发展轨迹。该分类学可实现针对特定场景的专业发展干预，并产生可检验的预测，推动元认知理论超越主要基于描述性记述的现有水平。