Social, Motivational, and Methodological Pathways to Behavioural Engagement and Psychological Regulation in AI ‐Enhanced EFL Learning

The Roles of Self‐Efficacy and Perceived Fairness

Dados Bibliográficos

ID22413285
AutoresYanyang Jin (0009-0008-0591-8204, Foreign Languages College, Inner Mongolia University Hohhot Inner Mongolia China), Jijun Wang (0000-0003-4536-9664, Foreign Languages College, Inner Mongolia University Hohhot Inner Mongolia China, autor correspondente)
Ano2026
Volume61
Fascículo3
Data de publicação2026-09-01
Open AccessSim
TipoARTICLE
PeriódicoEuropean Journal of Education (JOURNAL)
Identificadores do periódicoISSN: 0141-8211 • E-ISSN: 1465-3435
EditoraWiley (PUBLISHER • GB)
DOI10.1111/ejed.70764
OpenAlexW7168158986
IdiomaEN
Referências citadas52

The growing use of artificial intelligence (AI) in English as a Foreign Language (EFL) education contexts has reshaped how learners interact, stay motivated and manage their learning experiences. However, limited empirical work has examined how social, motivational, and methodological factors operate together to explain learner engagement and emotional regulation in AI‐enhanced EFL education contexts. This study explored the relationships among social interaction quality, learner self‐efficacy, task motivation, perceived methodological fairness of AI‐supported assessment, behavioural engagement and psychological regulation. Survey data were collected from 497 Chinese undergraduate EFL learners who had prior experience with AI‐assisted English learning. Structural equation modelling (SEM) was conducted using AMOS version 24 to test both direct and indirect relationships among the study variables. The results showed that social interaction quality was positively related to self‐efficacy, task motivation and perceived methodological fairness. Self‐efficacy and task motivation emerged as strong predictors of behavioural engagement, while perceived methodological fairness played a central role in predicting psychological regulation. The findings suggest that learner engagement in AI‐enhanced EFL settings develops through motivational and emotional pathways rather than through technology use alone. The study offers theoretical insights into engagement and social cognitive perspectives and provides practical guidance for the design and implementation of AI‐supported instruction and assessment in EFL classrooms.

Cognition · Empirical research · Quality (philosophy) · Structural equation modeling · Student engagement · Task (project management) · Test (biology) · Work engagement · AI in Service Interactions · Motivation and Self-Concept in Sports · Technology-Enhanced Education Studies