Vocational English for the Clothing Industry: Developing a Task-Based Quality-Control Communication Textbook
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Abstract
This study addresses critical quality-control (QC) challenges in the global industry, where communication barriers in cross-border supply chains frequently lead to rework, defects, and increased operational costs. A task-based vocational English training system for garment QC engineers was developed and implemented in a high-volume knitwear factory by integrating data-driven language instruction with industrial engineering methodologies. Using a modified Delphi process, 45 core garment inspection tasks were identified and organized into a four-dimensional competency framework encompassing technical, linguistic, cognitive, and cultural capabilities through Quality Function Deployment (QFD). The proposed system incorporates statistical process control (SPC), an augmented reality (AR) simulator for defect inspection, and a BERT-based semantic validation algorithm to ensure bilingual terminology consistency in QC documentation. An eight-week industrial evaluation demonstrated a 15.2% improvement in first article inspection (FAI) pass rate and a return on investment of 217%, while machine-learning analysis established a nonlinear relationship between language errors and rework costs. The AR platform achieved 90.9% speech-recognition accuracy under factory noise conditions, supporting reliable human–machine interaction in complex production environments. Beyond vocational education, the proposed framework provides an integrated solution for intelligent quality communication and digital manufacturing, offering methodological insights for robust information transmission, industrial sensing systems, and communication reliability in smart factories operating within increasingly integrated wireless and electromagnetic environments.
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