How Cognitive Factors and Motivation Shape Learners' Engagement in
AI
‐Supported Language Learning?: Insights From the Combined
SEM
and
fsQCA
Approaches
Aug 2026· European Journal of Education· Vol 61· 0 citations· 55 references
TL;DR
These findings provide theoretical support for SCT in technology‐mediated learning and suggest practical strategies for educators, including tailoring instructional design to learners' cognitive profiles, fostering motivation and self‐efficacy and leveraging AI tools to enhance active and sustained engagement in language learning.
Abstract
Artificial intelligence (AI) has increasingly been integrated into both classroom‐based and informal language learning, with tools such as AI chatbots, automated feedback systems and generative AI applications offering personalised, interactive and adaptive learning experiences. However, little is known about how learners' cognitive factors and psychological mechanisms are associated with engagement in AI‐supported learning environments. Guided by Social Cognitive Theory (SCT), this study investigated the roles of AI literacy, growth mindset and critical thinking disposition in predicting learners' motivation, self‐efficacy and engagement. A cross‐sectional design was employed, with 781 university students participating in structured questionnaires. Structural equation modelling (SEM) was used to examine direct and indirect effects, while fuzzy‐set qualitative comparative analysis (fsQCA) identified multiple configurations of cognitive and psychological factors associated with high engagement. SEM results indicated that all three cognitive factors are positively associated with motivation and self‐efficacy, which in turn significantly mediated engagement. The fsQCA findings demonstrated equifinality, showing that different combinations of cognitive and psychological factors can lead to similar high engagement outcomes. These findings provide theoretical support for SCT in technology‐mediated learning and suggest practical strategies for educators, including tailoring instructional design to learners' cognitive profiles, fostering motivation and self‐efficacy and leveraging AI tools to enhance active and sustained engagement in language learning.
With the growing integration of artificial intelligence into higher education, digital competence has become central to learners' meaningful engagement in AI‐mediated learning environments. However, its role may be constrained by the cognitive load generated when students navigate complex digital tools, resources, and learning tasks. This study, therefore, adopted an explanatory sequential mixed‐methods design to examine whether digital competence was associated with learners' engagement and whether cognitive load moderated this relationship. Quantitative data were collected from 339 Chinese university EFL learners through a questionnaire measuring digital competence, cognitive load and engagement. Regression analysis showed that cognitive load was negatively associated with engagement and significantly moderated the relationship between digital competence and engagement. In contrast, the direct effect of digital competence became non‐significant after these factors were considered. In the subsequent qualitative phase, interviews with 10 learners were used to explain and extend these findings, showing that digital competence supported engagement through richer resources, personalised learning, autonomy, and interaction, whereas information overload, distraction, AI dependence, unequal access, and privacy concerns constrained meaningful engagement. These findings suggest that digital competence should not be understood as a stand‐alone predictor of engagement, but as a conditional educational resource whose value depends on learners' ability to regulate cognitive load in AI‐mediated learning contexts. The study contributes to current discussions on digital transformation in higher education by highlighting the need for pedagogical support, critical AI literacy and equitable learning conditions that help students engage meaningfully with emerging technologies.
Jiachen Zhang, Siyi Wang· European Journal of Educatio...· 0 citations
Although generative AI is increasingly integrated into higher education, its impact on student’s learning experience remains unclear. This study examined factors predicting learning interactions in EFL (English as a Foreign Language) contexts, focusing on student’s AI competency, attitudes, and experience. Grounded in constructivist theory and the WEST model (Will, Experience, Skill, and Tools), a questionnaire was administered to 884 students at a Chinese higher vocational college. Structural equation modeling shows that AI integration and involvement in creative tasks directly predict learning interaction, while competency, attitudes, and experience exert indirect effects via these variables. Theoretically, this study provides quantitative evidence that student’s AI-related characteristics may contribute to learning interaction through AI-supported learning practices and creative task involvement. In practice, students can be more active in classroom interactions by adopting AI tools and participating in appropriate creative tasks designed by their teachers. Consequently, teachers play an important role in determining how AI tools are integrated and what types of tasks are assigned to students in the English classroom to support a better interactive learning environment.
As artificial intelligence (AI) becomes increasingly integrated into educational contexts, understanding how learners engage with AI-generated feedback is critical. Although prior research has examined the effectiveness of automated writing evaluation, less attention has been given to the cognitive and affective processes through which learners interpret and respond to such feedback. This study employed a constructivist grounded theory approach to investigate how Chinese undergraduate EFL learners engaged with AI-supported feedback during L2 writing revision. Participants were 24 English-major undergraduates at a public university in China. Data were generated through six semi-structured small-group interviews and analyzed through iterative open, axial, and selective coding. The analysis identified three interrelated domains—cognitive engagement, contextual evaluation, and affective regulation—which were integrated into an interpretive cognitive–affective dual-path model. When feedback was perceived as transparent, manageable, meaning-preserving, and aligned with task demands, learners demonstrated stronger cognitive engagement, greater willingness to experiment with complex structures, and sustained motivation. In contrast, feedback perceived as opaque, overly evaluative, or misaligned with learner expectations was associated with negative affect, selective uptake, risk avoidance, and strategic simplification. Learner evaluative filtering appeared to shape how noticing developed into revision action. These findings provide a process-oriented account of learner engagement in AI-supported writing environments and suggest that the influence of AI-generated feedback is filtered through learners' evaluative judgement, contextual appraisal, and affective regulation.
Ting Li, Azlina Binti Abdul Aziz, Nur Ainil Sulaiman· Frontiers in Psychology· 0 citations
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.
Yanyang Jin, Jijun Wang· European Journal of Educatio...· 0 citations
Guided by Social Cognitive Theory, this study explored how language learning curiosity, perceived teacher support, and AI literacy predict learners' self‐regulated learning (SRL) in AI‐supported language education, with grit and flow as mediators. Data collected from 1022 Chinese undergraduate students were analysed using descriptive statistics, confirmatory factor analysis, structural equation modelling with bootstrapped mediation analysis, and psychological network analysis via SPSS, AMOS, and R. The results showed that all three antecedent variables were positively associated with grit and flow, which in turn significantly mediated their influences on self‐regulated learning. Network analysis further revealed that self‐regulated learning was the most pivotal node among the six constructs. These findings suggest that effective AI‐supported language learning requires not only technological access, but also motivational, contextual, technological, and psychological resources to foster learners into agentic and self‐regulated language learners.
Qian Wang, Yongliang Wang· European Journal of Educatio...· 0 citations
AI is increasingly used in language learning, yet the psychological mechanisms underlying student engagement remain insufficiently understood. This study investigates the associations among teacher enthusiasm, learner growth mindset, and student engagement in AI‐mediated language learning, with a focus on the mediating roles of willingness to communicate, resilience and creative self‐efficacy. A total of 699 university students participated in a cross‐sectional survey, and the data were analysed using structural equation modelling and psychological network analysis to examine indirect pathways and networked associations among environmental, cognitive and psychological factors. The results indicate that higher levels of teacher enthusiasm and stronger growth mindset are positively associated with psychological variables, which in turn are linked to higher engagement. Network analysis further reveals that willingness to communicate, resilience and creative self‐efficacy are interconnected, with engagement occupying a central position within the network. These findings highlight the importance of considering multiple psychological mechanisms when exploring engagement in digitally enhanced learning contexts and extend social cognitive theory by illustrating the interactive contributions of environmental and individual cognitive factors. These findings extend social cognitive theory by showing how environmental and individual factors jointly shape engagement through multiple psychological mechanisms.
Xi Chen· European Journal of Educatio...· 0 citations