Exploring Learners’ Perceptions of AI‐mediated informal digital Multilingual Learning: A Q‐Methodology Approach

ABSTRACT Despite the growing integration of artificial intelligence (AI) in language learning, little is known about how multilingual learners perceive and engage with AI‐mediated informal digital language learning (AI‐IDLL). To address this gap, this study adopts the extended unified theory of acceptance and use of technology (UTAUT2) framework and employs Q‐methodology to investigate 31 Chinese university students learning both English and Japanese. The study identified three factors that influence students’ AI‐mediated informal digital learning of English (AI‐IDLE): performance‐driven motivation, emotion‐driven motivation, and the coexistence of quality anxiety and tool dependency. In contrast, two factors influence students’ AI‐mediated informal digital learning of Japanese (AI‐IDLJ): coexistence of cost concerns and efficiency‐driven motivations, and facilitating conditions. By comparing the motivational factors shaping the behavioral intentions in AI‐IDLE and AI‐IDLJ, this study reveals shared mechanisms and the culture‐specific dynamics in multilingual technology acceptance. These findings elucidate the diverse motivational factors influencing language learners in a multilingual context, contributing to theories of AI‐IDLL and informing pedagogical approaches in AI‐assisted multilingual teaching.