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.

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