Scalable AI tutoring for procedural skill learning requires structured knowledge representations, yet constructing these representations remains a labor-intensive bottleneck. This paper presents a human-in-the-loop text-to-model pipeline that uses large language models to transform instructional materials into schema-complete Task-Method-Knowledge models of procedural skills through ontology-constrained prompting and template-based generation. The approach automates structural scaffolding while preserving expert oversight for validating causal transitions and failure conditions. We apply the pipeline to instructional materials from a graduate-level online AI course, constructing 23 procedural skill models. AI-assisted authoring reduced expert modeling time by 50-70% while producing structurally valid and highly reproducible models under fixed-input conditions. We evaluate structural validity, semantic alignment, reproducibility, and refinement effort to characterize authoring scalability. Results indicate that AI-assisted text-to-model methods can substantially lower the cost of constructing structured procedural representations, making course-wide deployment of structured AI coaching systems practically feasible.

翻译：可扩展的人工智能辅导系统在程序性技能学习中需要结构化知识表征，然而构建这些表征仍是劳动密集型瓶颈。本文提出一种人机协同的文本到模型流水线，通过本体约束提示和模板生成，利用大语言模型将教学材料转化为模式完整的任务-方法-知识模型。该方法在自动化结构构建的同时，保留专家对因果转换和失败条件验证的监督权。我们将该流水线应用于研究生在线人工智能课程的教学材料，构建了23个程序性技能模型。在固定输入条件下，人工智能辅助创作使专家建模时间减少50-70%，同时生成的模型具有结构有效性和高度可复现性。我们通过评估结构有效性、语义对齐性、可复现性及精炼工作量来表征创作可扩展性。结果表明，人工智能辅助文本到模型方法可显著降低结构化程序性表征的构建成本，使课程级结构化人工智能辅导系统的部署具备实际可行性。