Human-AI collaboration in vocational writing: Building a framework for adaptive English learning

Abstract

The rapid technological advancements of the 21st century have substantially altered many aspects of education, including language learning and acquisition. Developments in Artificial Intelligence (AI) have reshaped perspectives on language education, offering new pedagogical opportunities. Numerous studies have investigated AI-powered tools to improve language proficiency. However, these studies mainly focus on general English. There is a gap in the literature regarding the application of AI-powered approaches for teaching Vocational English, particularly in developing writing skill within vocational contexts. This paper presents a theoretical framework that explains how AI-powered tools can be integrated into vocational language education to enhance writing proficiency. The framework combines theories of second language acquisition, socio-constructivist learning, and adaptive learning environments. It proposes that AI acts as a mediating agent by enhancing learner autonomy, providing scaffolded writing practice, and enabling individualized feedback. These aspects are particularly important for vocational language needs. The model identifies three core dimensions based on a synthesis of recent literature: personalization, contextualization, and digital feedback. By outlining these dimensions, the framework provides structured objectives for future studies and curriculum design. This paper also highlights the importance of ethical AI implementation, data privacy, and technology availability in supporting the specialized writing skills needed for learners of English for Specific Purposes (ESP).

Keywords

• artificial intelligence
• vocational English
• writing skill
• theoretical framework
• educational technology

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