AI Literacy Among Chinese Vocational EFL Learners: A Qualitative Exploration of Motivation, Technology Acceptance, and Goal Orientation

Abstract

The rapid integration of artificial intelligence (AI) into higher education has heightened the need for students to develop AI literacy as a core competence for both academic performance and future employability. Drawing on Expectancy-Value Theory, the Technology Acceptance Model (TAM), and Goal Orientation Theory, this qualitative case study explores the factors influencing AI literacy among English as Foreign Language (EFL) learners at a vocational college in Guangdong, China. Data were collected through semi-structured interviews with 10 participants for in-depth analysis. Using NVivo 14, thematic analysis revealed three overarching themes: (1) motivation (attainment value and intrinsic interest), (2) mastery goal orientation, and (3) technology acceptance (perceived usefulness and perceived ease of use). Findings indicate that high task value and intrinsic interest strongly enhance AI literacy, mastery goal orientation promotes deeper engagement with AI tools, and perceived usefulness is the strongest predictor of sustained adoption, while perceived ease of use primarily affects initial acceptance. The study provides a conceptual model for EFL learners’ AI Literacy to offer practical implications for designing AI-integrated EFL curricula in vocational contexts.