Pre-service English teachers' Artificial Intelligence literacy and its learning solution—A mixed-methods research

Abstract

In the context of generative-AI-driven educational transformation, pre-service EFL teachers' GenAI literacy is vital for future professional competency. This convergent parallel mixed-methods study administered a 34-item GenAI Literacy Questionnaire and conducted semi-structured interviews with 88 pre-service EFL teachers from southeast China and five outstanding peers. Guided by Bronfenbrenner's Ecological Systems Theory, we examined the overall status, dimensional structure, and learning pathways of GenAI literacy. Quantitative results showed generally positive self-evaluations, with prominent ethical awareness but weak theoretical mastery and offline instructional-design capacity. Exploratory factor analysis verified a five-dimensional construct. Qualitative analysis revealed dual learning channels and grade-differentiated demands. Synthesizing evidence, we propose a dual-channel staged progressive learning pathway integrating university-based formal training with autonomous informal practice. This study offers empirical evidence for optimizing pre-service EFL teacher education and AI-related professional training.