Exploring the Impact of Affordance Theory on Motivated Learning Behavior in ChatGPT-assisted IDLE Contexts

Given ChatGPT's growing significance in education, understanding how learners interact with this technology is quite crucial. However, few studies focus on how various factors interplay in ChatGPT-assisted informal digital learning of English (IDLE) contexts. To address this gap, the study employed a quantitative research design using questionnaires. The participants were 944 Chinese EFL learners who completed a structured questionnaire measuring perceived affordances, digital literacy, grit, emotions (burnout and foreign language enjoyment (FLE)), and motivated learning behavior. Based on affordance theory, the study investigated how these variables influence motivated behavior towards ChatGPT-assisted IDLE. The results indicated a decreased level of burnout and moderate to high levels of perceived affordances, digital literacy, grit, FLE, and motivated learning behavior. Data analysis was conducted using structural equation modeling (SEM) to assess the relationships between these variables. It was found that while digital literacy acted as a mediator between perceived affordances and motivated learning behavior, it served as a strong serial mediation between FLE and digital literacy. However, there was no mediating role for either burnout or grit in the relationship between digital literacy and motivated learning behavior. The study emphasizes the importance of a variety of elements when determining motivated behavior in ChatGPT-assisted IDLE. The results offer significant implications for educators and policymakers, emphasizing the need for tailored interventions to improve digital literacy and foster emotional experiences, thus sustaining motivated behavior toward ChatGPT-assisted English learning contexts.