Unpacking the role of motivation and enjoyment in AI-mediated informal digital learning of English (AI-IDLE): A mixed-method investigation in the Chinese context

Abstract

This paper examines how Chinese university students negotiate their second language (L2) motivational dynamics, including their ideal and ought-to L2 selves, to participate in informal digital learning of English (IDLE) mediated by generative artificial intelligence (AI). It demonstrates the extent to which enjoyment, the most observable positive emotion in L2 learning, influences their involvement in AI-mediated IDLE (AI-IDLE) activities. Employing an explanatory sequential mixed-method design, this study surveyed 690 Chinese undergraduate students and conducted 12 post-survey interviews. Using a structural equation modeling approach, the quantitative analysis reveals that participants’ ideal L2 self can significantly predict both their sense of enjoyment and AI-IDLE, while the ought-to L2 self is only able to directly predict enjoyment. The quantitative results also demonstrate that enjoyment can partially mediate the relationship between the ideal L2 self and AI-IDLE and simultaneously fully channel the indirect impact of the ought-to L2 self on AI-IDLE. Supplementing these quantitative findings, the interview data provides a nuanced understanding of how motivation and enjoyment shift and interact with learning contexts as participants engage in AI-IDLE. Drawing on these quantitative and qualitative insights, this study identifies implications for pedagogy, particularly in terms of motivating Chinese university students to engage in IDLE while maintaining emotional well-being in the age of generative AI.