Emotion regulation as a developmental process in GenAI-assisted learning: Insights from Vietnamese EFL learners

The rapid integration of generative artificial intelligence (GenAI) tools in language education has introduced new emotional demands for learners, yet little research has examined how students regulate their emotions when learning with these tools. This study investigates emotion regulation strategies among Vietnamese EFL learners in GenAI-assisted environments. Drawing on Gross's (1998, 2015) process model of emotion regulation and Vygotsky's (1994) concept of perezhivanie, it conceptualizes emotion as part of learners' lived and developmental experience rather than a separate affective outcome. A total of 255 undergraduate and graduate students completed an adapted Emotion Regulation Questionnaire (Gross & John, 2003) tailored to GenAI-mediated English learning. Data were analyzed using descriptive statistics, paired-samples t-tests, MANOVA, and Type-III ANOVAs. Learners reported moderate use of both cognitive reappraisal and expressive suppression, with reappraisal slightly more frequent. Year of study was significantly associated with both strategies, first-year students reporting the lowest levels. Region was associated with reappraisal, though without clear pairwise differences. Gender, English major status, proficiency, GenAI use frequency, and IT access were not significant predictors. These findings suggest that emotion regulation is shaped more by academic experience than technology access. The study proposes a developmental–sociocultural model emphasizing emotional literacy through scaffolding, guided reflection, and reappraisal-focused feedback.