Reconsidering the role of L2 self-motivational and affective factors in AI-mediated informal digital learning of English: a mixed-methods study - Li, Haomin Tommy; Yan, Ping (Frontiers in Psychology, 2026)
IntroductionGrowing scholarly interest has emphasized the significance of non-cognitive variables in influencing learners’ informal English acquisition through artificial intelligence (AI)-enhanced digital platforms. Despite this, research remains sparse on how motivational constructs, emotional responses, particularly enjoyment, and anxiety resulting from AI-assisted learning (AAL), and participation in AI-mediated informal digital English learning (AI-IDLE) collectively contribute to learners’ self-efficacy beliefs in speaking English as a foreign language (EFL).MethodsEmploying an explanatory sequential mixed-method design, this study surveyed 308 EFL secondary students in China, followed by in-depth interviews with eight voluntary respondents. Quantitative data were analyzed using structural equation modeling, while qualitative data underwent thematic analysis involving both open and axial coding procedures.ResultsPath results demonstrated that AAL negatively predicted EFL learners’ AI-IDLE engagement. However, AI-IDLE exerted no statistically significant influence on students’ self-reported self-efficacy in spoken English. Notably, enjoyment emerged as a mediator in the paths from the ideal second language (L2) self to both AI-IDLE and self-efficacy beliefs in speaking capacity. Qualitative data based on narrative descriptions evinced the heterogeneity of motivational and emotional factors of students operating in AI-supported informal learning situations.DiscussionThrough a combination of quantitative results with interpretative reflections, this study presents some useful guidelines for maximizing student participation with respect to AI-IDLE, ensuring AI literacy, and protecting their emotional wellbeing through the rapidly developing environment of generative AI.