AI-EMPOWERED PRODUCTION-ORIENTED APPROACH AND FIVE-STAGE INSTRUCTIONAL MODEL IN HIGHER VOCATIONAL ENGLISH: A CASE STUDY OF UNIT 3 “PROFESSIONALISM”
AI-EMPOWERED PRODUCTION-ORIENTED APPROACH AND FIVE-STAGE INSTRUCTIONAL MODEL IN HIGHER VOCATIONAL ENGLISH: A CASE STUDY OF UNIT 3 “PROFESSIONALISM”

Summary/Abstract: With the rapid development of artificial intelligence, its integration into English language teaching has become an inevitable trend. Wen (2015) proposed the Production Oriented Approach as a pedagogical theory rooted in the Chinese context. The theory effectively tackles the long‑standing problem of “separation of learning and application” in foreign language education. However, applying the Production Oriented Approach to higher vocational English teaching is not straightforward, common challenges include insufficient teaching resources, a lack of personalized scaffolding for students, and delayed feedback from assessments. To address these challenges, this study aims to construct an artificial intelligence empowered instructional design framework that integrates the Production Oriented Approach with the five stage instructional model (Engage, Explore, Explain, Elaborate, Evaluate) by Bybee et al. (2006) and to demonstrate its application through a case design. A case study approach was adopted. The study took place in the autumn semester of 2025 at Sichuan Vocational College of Information Technology at the author’s institution in Guangyuan of China, involving a class of 50 first‑year higher vocational students. Taking Unit Three “Professionalism” from a higher vocational English textbook New Career English 1 as the context, the final output task writing a narrative essay titled “My Role Model” was broken down into four progressive subtasks across four teaching sessions. These subtasks follow a cognitive progression: macro understanding→micro focusing→internalizing cultivation→externalizing action. Various Artificial intelligence technologies (e.g., for vocabulary learning, speech recognition, text analysis, grammar checking, writing assistance, translation, and learning analytics) were woven into each session within the five stage instructional cycle. The results show that this artificial intelligence empowered integrated model works well. It effectively boosts students’ learning motivation and optimizes the Production Oriented Approach based cycle of “Motivating Enabling Assessing.” The subtask decomposition reduces cognitive load and provides clearer learning pathways, while real‑time artificial intelligence generated feedback helps students make timely improvements. Notably, over ninety percent of the students successfully completed the final output task, which suggests the design is feasible. It is concluded that the artificial intelligence empowered integrated model is a promising instructional approach for higher vocational English, as it resolves key difficulties in traditional Production Oriented Approach implementation. The progressive subtask design aligns well with Production Oriented Approach’s enabling principle and supports the deeper integration of language learning with professional ethics education. Based on these findings, this study recommends: enhancing teachers’ artificial intelligence literacy, adopting subtask decomposition strategies, providing institutional training on artificial intelligence tools with due attention to data privacy protection, and following a “Human‑Machine Collaboration” model. This study offers a detailed case design along with visual frameworks, which can serve as a practical reference for instructional innovation and implementation

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