Keywords
artificial intelligence
foreign language enjoyment
foreign language boredom
strategic competence for cross-cultural communication
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Abstract
Artificial intelligence-mediated informal digital learning of English (AI-IDLE) has received increasing attention in foreign language learning. However, few studies have integrated variable-centered and person-centered approaches to investigate how AI-IDLE relates to achievement emotions and strategic competence for cross-cultural communication (SCCC). The present study therefore examined the associations between AI-IDLE and SCCC through two achievement emotions, specifically enjoyment and boredom, using both analytical approaches. Participants were 435 Chinese university EFL undergraduate students. In Stage 1, a variable-centered analysis was conducted to explore the connections among AI-IDLE, foreign language enjoyment (FLE), foreign language boredom (FLB), and SCCC. The results showed that AI-IDLE directly and positively predicted both FLE and SCCC, while negatively predicting FLB. FLE partially mediated the relationship between AI-IDLE and SCCC. By contrast, FLB did not significantly predict SCCC. In Stage 2, a person-centered analysis was undertaken to identify distinct profiles of students’ AI-IDLE. Results demonstrated that three profiles were identified: passive AI-IDLE users, balanced AI-IDLE learners, and proactive AI-IDLE strategists. Specifically, the profile of balanced AI-IDLE learners accounted for the largest proportion (N = 206, 47.3%). Theoretical and pedagogical implications were discussed.
Funding
This study was supported by Macao Polytechnic University research grant: Understanding Metacognition in GenAI Use for Language Teaching and Research (Grant No. RP/FLT-05/2026) awarded to the second author: Mark Feng Teng. This study is also supported by the Hunan Province Key Teaching Reform Project for Regular Undergraduate Universities, Research on Inno-vative Approaches and Models of College Foreign Language Education and Instruction in the Context of Generative Artificial Intelligence (Project No. 202502002177), awarded to the third author, Xueyun He.
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