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Under-resourced EFL students’ perceptions about the causes and consequences of unfair AI-mediated education
(Taylor & Francis, 2025) Wanga, Yongliang; Lib, Hang; Savaş, Hasan
The use of Artificial Intelligence (AI) technologies in education imposes various social influences on different stakeholders across diverse contexts. However, the voices of under-resourced second language (L2) learners have remained unheard regarding the fairness of AI adoption. To fill this gap, the present qualitative study examined 33 Chinese English as a foreign language (EFL) students’ perceived causes and consequences of unfair AI-mediated education. Thematic analysis of online interviews indicated four causes and four consequences for unfair AI adoption in under-resourced communities. The causes included biased algorithms and databases, digital divide and unequal access, lack of AI-related training and support, and sociocultural mismatch and inappropriateness of AI tools in poor settings. Regarding consequences, it was found that unfair AI adoption may lead to educational inequality, diminished motivation, academic deskilling, and technophobia among under-resourced EFL students. The findings are discussed, and implications for raising AI literacy and readiness of L2 educators and policymakers are enumerated.