Leveraging translanguaging in GenAI-enhanced language classes: capturing its impact on multilingual English learners’ achievement emotions and academic engagement through latent growth curve modelling (LGCM)

Abstract

The present research traced the growth trajectories of multilingual English learners’ achievement emotions and academic engagement in generative artificial intelligence (GenAI)-enhanced classes and examined the effects of translanguaging pedagogy as an emotional scaffolding strategy in this context. Using purposive sampling, 342 multilingual English learners were selected and randomly assigned to either a control group or a treatment group. Data were collected at three time points using two validated questionnaires and analyzed via latent growth curve modelling (LGCM) and repeated measures multivariate analysis of variance (RM MANOVA). The analysis exhibited an upward trajectory in the development of learners’ positive achievement emotions and academic engagement. The results further indicated that translanguaging significantly promoted learners’ positive achievement emotions and reduced their negative achievement emotions, which, in turn, increased their academic engagement. These findings highlight the contributions of translanguaging pedagogy in GenAI-enhanced classrooms and call for more linguistically responsive instructional designs that recognise and build on learners’ multilingual repertoires.

The authors' abstract, as published at the source. International Journal of Multilingualism, 2026 · DOI ↗