A Study on Generative AI Empowering Vocational Undergraduate English Writing Instruction

Abstract

With the rapid development of generative Artificial Intelligence (AI) technology, its application potential in the field of education is becoming increasingly prominent. Vocational undergraduate English writing instruction, which emphasizes practicality and vocational orientation, encounters limitations in traditional teaching models in terms of personalized feedback, learning efficiency, and the construction of vocational contexts. Based on constructivist learning theory, sociocultural theory, and mastery learning theory, this study constructs a theoretical framework for the integration of generative AI into English writing instruction, systematically elaborating on its intrinsic mechanisms and structural dimensions. Building upon this foundation, the study proposes an empowerment model with intelligent agents, data flow, and contextual design as its core components. It develops a three-layer teaching structure consisting of foundational support, core activities, and top-level guidance. Additionally, it outlines a three-phase iterative implementation process covering preparation, intervention, and integration, accompanied by a multidimensional comprehensive efficacy evaluation system. The study further analyzes practical challenges such as technical reliability, instructional adaptability, and academic integrity, proposing optimization strategies from technical, instructional, managerial, and support levels. Finally, it suggests sustainable development directions focusing on educational technology ecosystem construction, integration of research and practice, and future-oriented literacy cultivation, aiming to provide theoretical references and practical pathways for the innovation and reform of English writing instruction in vocational undergraduate education.