Speaker
Description
ABSTRACT
Generative AI is now woven into everyday teaching, learning, and assessment, but its benefits haven't landed equally. Cultural bias and narrow linguistic representation remain embedded in many AI systems, and current AI literacy frameworks do little to address this—they focus almost entirely on technical competence, leaving out whether teachers and learners can actually recognize biased outputs or exercise ethical judgment within their own cultural context. This study examines how AI literacy can be reoriented around ethical decision-making, cultural responsiveness, and equitable assessment practices, with a focus on higher education teachers and students in Vietnam. The research uses a mixed-methods design involving university teachers and students, combining surveys, classroom-based interventions, and semi-structured interviews. Quantitative data will be analyzed using descriptive statistics and structural equation modeling, while qualitative data will undergo thematic analysis. The study is expected to yield an AI literacy framework grounded in ethical awareness, critical evaluation of cultural bias, and multilingual inclusivity—offering educators and institutions concrete, evidence-based directions for building AI-supported education that is fairer, more culturally responsive, and more linguistically inclusive.
Keywords: ai literacy, cultural bias, linguistic diversity, ai-assisted assessment, inclusive education
