Linking growth mindset to engagement via ideal L2 self and enjoyment in AI-mediated EFL writing: a mixed-methods study

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Authors: Hui Li, Shengxuan Geng
Cited by: 1
References: 61

Abstract

This study investigates the psychological mechanisms underlying engagement in AI-mediated EFL writing from a social cognitive theory perspective. Specifically, it examines how growth mindset, ideal L2 self, and enjoyment relate to engagement. An explanatory sequential mixed-methods design was employed. In the quantitative phase, survey data were collected from 519 university EFL learners from ethnic minority regions in China and analyzed using structural equation modeling. In the qualitative phase, semi-structured interviews were conducted with 20 participants to elaborate on learners' experiences and explain the quantitative results. The quantitative findings revealed significant positive associations among growth mindset, ideal L2 self, enjoyment, and engagement in AI-mediated EFL writing. Mediation analyses further indicated that growth mindset was indirectly related to engagement through ideal L2 self and enjoyment, including both single-mediator pathways and a chained pathway via ideal L2 self followed by enjoyment. The qualitative findings further showed that learners maintained engagement by reframing challenges and feedback as learning opportunities, connecting writing to future self-guides, and experiencing enjoyment during iterative revision. This study advances understanding of engagement in AI-mediated EFL writing by highlighting the interplay between personal beliefs, motivational self-guides, and affective experiences, and it offers practical implications for designing AI-supported writing instruction that fosters sustained learner involvement.

Fields of Study & Subjects

EFL/ESL Teaching and Learning Grit Self-Efficacy and Motivation Writing and Handwriting Education

Publication details

Journal / source
Frontiers in Psychology
Publisher
Frontiers Media SA
Volume
17
Pages
1836353–1836353
Publication date
16 June 2026
Language
EN