Untangling the Nuances and Networks of Mobile-GenAI English Learning

A study of Arab Immigrant and Refugee Women in Canada

Abstract

Networked learning is defined as learning supported by purposeful connections between people, resources, and technologies (NLEC, 2020). The preliminary results of the research described in this paper focuses on Arab immigrant and refugee women (AIRW) who live in the Canadian province of Saskatchewan and their experiences using mAIL technology to learn English. Kearney et al.’s (2012) sociocultural pedagogical framework guided this study. As the research progressed, it became apparent that it is not possible to simply layer GenAI upon mobile learning; rather, the use of AI changed the nature and networking patterns of the learners. This phenomenological study illustrates how mobile GenAI (mAIL) technology alters the admixture of networked learning in both general and subtle ways. In a general sense, the very nature of the large language models (LLMs) is inherently networked as it connects a vast collection of human-produced artefacts/resources (mostly text based) to human learners who access these artefacts via prompts. We suggest that current models of mobile learning can be shifted to better reflect the nuanced interactions.