Speaker
Description
As generative artificial intelligence becomes embedded in language classrooms, teacher education programs face urgent questions about how prospective teachers are being prepared to use AI ethically, critically, and pedagogically. This work-in-progress study investigates the AI literacy readiness of Year 3 and Year 4 pre-service EFL teachers at the University of Languages and International Studies, Vietnam National University, Hanoi (ULIS-VNU), addressing three questions: their current level of readiness, the factors predicting that readiness, and the learning needs they report for professional training. Readiness is conceptualized through a novel ABCE framework: Affective, Behavioral, Cognitive, and Ethical operationalized via the Adapted AI Literacy Readiness Scale, while predictor variables (AI self-efficacy, prior experience, institutional support) draw on TAM/UTAUT, and learning needs are measured through an I-TPACK-based gap-analysis scale. The study employs an embedded, quantitatively dominant mixed-methods design, QUAN(qual), administered as a cross-sectional survey to approximately 300 respondents, supplemented by open-ended items and twelve semi-structured interviews. Grounded in the empirical pattern documented across Chinese, German, Slovak, and Vietnamese contexts, the study anticipates an asymmetric readiness profile in which behavioral and affective preparedness outpace cognitive and ethical preparedness, with self-efficacy and institutional support emerging as significant predictors and unmet needs concentrated in evaluative and ethical competencies. By translating readiness findings into a prioritized, gap-based training agenda, the study offers empirically grounded implications for embedding AI literacy, particularly its ethical and evaluative dimensions, into EFL teacher preparation curricula in Vietnam and comparable contexts.
