Enhancing professional competency development: Construction and application of an AIGC-empowered TFS-SRIE model in coffee preparation course - Liu, Cenyang; Cheng, Xizhu; Zhang, Yunyao (International Journal of Educational Technology and Learning, 2026)
With the global proliferation of digital technologies, generative artificial intelligence (AIGC) has been widely adopted in vocational education, offering new solutions to persistent instructional challenges in skills-based courses. Guided by competency-based education and connectivist learning theory, this study proposes the TFS-SRIE practical model for the secondary vocational Coffee Preparation course. The model is supported by three assurance dimensions—technical specifications, faculty capacity, and school–enterprise collaboration—and implemented through four pathways: scenario development, resource generation, instructional adaptation, and evaluation construction. Using the Coffee Preparation Course as an illustrative case, the paper details the model’s implementation procedures and teaching strategies, and evaluates its effectiveness in enhancing learning motivation and interest, enabling personalized learning, deepening skills mastery, and fostering innovative competence. The findings suggest that the TFS-SRIE model provides a transferable and actionable framework for integrating AIGC into secondary vocational curricula, contributing practical insights for advancing digital transformation and professional competency development in vocational education.