Profile of EFL Learners’ AI‐Mediated Informal Digital Learning of English, Digital Nativity, Desire, and Online Self‐Efficacy: Associations With Demographic Features

This study examines the profiles of learners of English as a foreign language within AI‐mediated informal digital learning of English (IDLE) environments, emphasizing AI‐mediated IDLE, digital nativity, desire, and online self‐efficacy, as well as their correlations with demographic characteristics. Using latent profile analysis (LPA), 1994 Chinese university students were categorized into three profiles: versatile digital natives, selective digital navigators, and emerging digital natives. Versatile digital natives showed high engagement in AI‐mediated IDLE activities, a strong desire for English learning, and robust online self‐efficacy. Selective digital navigators preferred structured and receptive IDLE activities, showing moderate desire and self‐efficacy. In contrast, emerging digital natives had lower engagement and digital proficiency, but they showed potential in interactive activities. Significant differences were found across profiles in terms of gender, digital device duration, and frequency of AI‐mediated IDLE use, and location, but not in age, using the R3STEP command. The findings highlight the need for tailored educational strategies to support diverse learner profiles, thereby enhancing language learning outcomes in digital contexts. This study offers insights for educators and policymakers to optimize language learning practices among digital natives.