Jul 2026· European Journal of Education· 0 citations· 37 references
TL;DR
The findings suggest that student engagement in AI‐assisted learning is linked not only to the availability of technological and interpersonal support, but also to the extent to which students experience learning as focused, absorbing and meaningful.
Abstract
With the increasing integration of artificial intelligence (AI) in higher education, this study examined how AI support, teacher support and flow are associated with student engagement in AI‐assisted learning in higher education. Drawing on Self‐Determination Theory, a cross‐sectional survey was conducted with 677 university students from multiple institutions who had prior experience using AI‐enabled tools for learning. Data were analysed using structural equation modelling with AMOS 26.0 to examine the hypothesised relationships among the study variables. The results showed that AI support and teacher support were both positively associated with student engagement. In addition, both forms of support were positively associated with flow and flow was positively associated with engagement. Mediation analyses further indicated that flow mediated the associations between AI support and engagement and between teacher support and engagement. These findings suggest that student engagement in AI‐assisted learning is linked not only to the availability of technological and interpersonal support, but also to the extent to which students experience learning as focused, absorbing and meaningful. The study contributes to current research by integrating technological support, teacher support and experiential processes into a single framework for understanding engagement in AI‐assisted learning in higher education.
Artificial intelligence (AI) is increasingly reshaping higher education, requiring university teachers to adapt their pedagogical roles, technological competence, and professional engagement. Drawing on the Job Demands–Resources (JD‐R) model, this study examined how organizational support and AI literacy were associated with university teachers' work engagement in AI‐supported teaching, with professional identity, resilience, and self‐efficacy as mediating personal resources. A questionnaire survey was conducted among 1135 Chinese university teachers with experience in AI‐supported teaching. Data were analysed using structural equation modelling (SEM), mediation analysis, and network analysis. SEM results showed that organizational support and AI literacy were positively associated with professional identity, resilience, and self‐efficacy, which were further positively associated with work engagement. Mediation analysis indicated that professional identity, resilience, and self‐efficacy mediated the relationships between organizational support, AI literacy, and work engagement. Network analysis further revealed that work engagement was the most central node, with self‐efficacy showing the strongest connection with engagement. These findings extend the JD‐R model to AI‐supported higher education and highlight the importance of institutional support, AI literacy development, and psychological resource building in enhancing teachers' engagement.
Xinxing Wu· European Journal of Educatio...· 0 citations
Introduction Artificial intelligence (AI) is increasingly used in education, yet less is known about the psychological processes through which AI-supported teaching relates to student engagement, especially in vocational education. Drawing on Self-Determination Theory, this study examined the mediating roles of perceived competence and perceived autonomy in the relationship between AI-supported teaching and student engagement. Methods A 10-week quasi-experimental study was conducted with 148 second-year vocational college students, assigned to either an AI-supported teaching group (n = 74) or a conventional instruction group (n = 74). Multimodal data were collected, including academic assessments, performance evaluations, self-report measures of perceived competence, perceived autonomy, and engagement, as well as behavioral records from AI-supported learning activities. Mediation analysis was performed using bootstrapping with 5,000 resamples. Results Students in the AI-supported teaching group showed higher levels of student engagement than those in the conventional instruction group (Cohen's d = 0.84). Perceived competence showed a statistically significant indirect association between AI-supported teaching and student engagement [β = 0.22, 95% CI (0.13, 0.31)], supporting H2. In contrast, perceived autonomy did not show a statistically significant indirect effect [β = 0.02, 95% CI (−0.02, 0.06)], and therefore H3 was not supported. The direct association between AI-supported teaching and engagement remained significant after accounting for both mediators. Discussion These findings suggest that competence-related experiences may represent an important psychological pathway linking AI-supported teaching and student engagement in structured vocational education contexts. The non-significant role of perceived autonomy indicates that motivational processes in AI-supported learning may vary across instructional settings and should be interpreted in relation to contextual and pedagogical conditions. Given the quasi-experimental design and the context-specific sample, the findings should be interpreted cautiously and further examined through larger, multi-site, andlongitudinal studies.
As artificial intelligence (AI) becomes increasingly embedded in higher education, student engagement in AI‐supported learning has become an important indicator of learning quality and educational effectiveness. However, existing research has paid relatively limited attention to the psychological mechanisms through which students become actively involved in AI‐supported learning environments. Grounded in control‐value theory, this study examined the relationships among growth mindset, enjoyment, hope, pride, and engagement in AI‐supported learning among university students. A questionnaire survey was conducted with 563 students who had prior experience of AI‐supported learning. Structural equation modelling was used to test the hypothesized relationships and mediating effects, while artificial neural network analysis was further employed to examine nonlinear predictive importance. The results showed that growth mindset was positively associated with enjoyment, hope, pride, and engagement. Enjoyment, hope, and pride were also positively associated with engagement and significantly mediated the relationship between growth mindset and engagement. The ANN results further indicated that pride was the strongest predictor of engagement, followed by growth mindset, enjoyment, and hope. These findings suggest that students' engagement in AI‐supported learning is associated not only with their beliefs about ability development, but also by their positive emotional experiences. The study contributes to educational research by offering an integrated explanatory and predictive account of student engagement in AI‐enhanced higher education.
Xiaoning Wang, Zixian Hong, Wei Guo· European Journal of Educatio...· 0 citations
The skill-based dimensions of AI literacy were positively associated with AI dependency, whereas AI self-efficacy and academic self-efficacy were both negatively associated, suggesting a unified compensatory self-efficacy mechanism.
With the widespread adoption of artificial intelligence (AI), the usage of various AI tools by university students to support learning has become increasingly common in classroom settings. Focusing on routine in-class AI usage and learning engagement, this study explores the mediating role of perceived learning engagement benefits in linking students’ AI usage behavior to their attitudes, and investigates whether its mediating effects differ across various dimensions of learning engagement. Based on data from 287 questionnaires, structural equation modeling and Bayesian analysis were used to test the proposed hypotheses. The results indicate that in-class AI usage was associated with students’ attitudes both directly and also indirectly by enhancing cognitive engagement and emotional engagement, both of which exhibited comparable mediating effects. In general, the university students exhibited positive attitudes toward in-class AI usage, and these attitudes were positively associated with their perceived learning engagement benefits. These results suggest that the value of AI in classroom settings may relate not only to learning efficiency, but also to students’ learning experiences, particularly in terms of perceived cognitive and emotional engagement benefits. Notably, although in-class AI usage was positively associated with all dimensions of perceived engagement benefits, perceived behavioral engagement benefits showed no significant indirect effect on attitudes. This null finding warrants further exploration in future research.
Wenqiang Fan, Jiaqi Fang, Yanlin Sun et al.· PLoS ONE· 0 citations
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