AI Integration in Vocational EFL in China: A Qualitative Pedagogical Capability Framework from Teachers’ Perspectives - Yi Xia; Saedah Binti Siraj; Siti Hajar Binti Halili (International Journal of Environment, Engineering and Education, 2026)
Artificial intelligence (AI) is increasingly reshaping English as a Foreign Language (EFL) instruction, yet its integration in vocational education remains insufficiently explained by adoption-oriented or tool-centered models. In vocational EFL, teachers must ensure that AI-supported learning is pedagogically meaningful, workplace-relevant, and ethically governed. This study develops an empirically informed pedagogical capability framework for integrating AI into vocational EFL teaching in China, from teachers’ perspectives. A qualitative exploratory design was employed with 41 vocational EFL teachers from six vocational and technical institutions in Yunnan Province, China. Data were collected through 18 semi-structured interviews and 23 extended open-ended survey responses. Reflexive thematic analysis was used to generate themes and synthesize framework dimensions, supported by coding summaries, source comparison, and reflexive memoing. Three themes emerged: teacher pedagogical capability and readiness, institutional and ethical barriers, and instructional applicability of AI tools. Teachers distinguished general digital competence from AI-specific pedagogical judgment, particularly in validating AI feedback, designing workplace-oriented tasks, protecting learner agency, and addressing privacy, authorship, bias, and overreliance. AI tools were perceived as useful for writing feedback, chatbot-based workplace simulation, pronunciation practice, adaptive learning, learner analytics, and occupation-specific vocabulary support, but only under teacher mediation and institutional support. Meaningful AI integration in vocational EFL depends on four interdependent dimensions: teacher pedagogical capability, institutional readiness, instructional applicability, and ethical governance. The framework extends TPACK, TAM, UTAUT, digital competence, and AI literacy by repositioning AI integration as a relational, human-centered, and context-sensitive pedagogical process.