The findings suggest that competence-related experiences may represent an important psychological pathway linking AI-supported teaching and student engagement in structured vocational education contexts and 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.
Abstract
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.
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.
Ping Xu, Yongbi Zhi· European Journal of Educatio...· 0 citations
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
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.
Objective: To develop a conceptual model examining the relationships between AI learning support, digital competence, teacher AI guidance, and learning performance among students in Malaysian higher education institutions. Methods: The proposed study adopts a quantitative cross-sectional design. Data will be collected through a survey of Malaysian university students, and the hypothesised relationships will be tested using structural equation modelling. The model includes academic engagement and self-regulated learning as sequential mediators and academic integrity concern as a moderator of the relationship between academic engagement and learning performance. Results: Drawing on self-regulated learning theory, student engagement theory, social cognitive theory, AI literacy, digital competence, and academic integrity literature, the proposed model suggests that AI learning support does not automatically improve students’ learning performance. Its effectiveness depends on students’ ability to use AI strategically, critically, and ethically, together with appropriate guidance from lecturers. Conclusion: The proposed model contributes to the AI education literature by shifting attention from students’ intention to adopt AI towards the learning mechanisms through which AI use may influence academic outcomes. It also provides a framework for understanding the roles of academic engagement, self-regulated learning, teacher guidance, digital competence, and academic integrity in AI-supported learning.
Raden Azamry Bin Raden Perhan, Rajoo Ramanchandram, Saralah Devi Mariamdaran Chethiyar et al.· Journal of Psychology &...· 0 citations
The emergence of artificial intelligence (AI) in education has not only challenged conventional learning environments but has also encouraged the adoption of innovative and active pedagogical approaches, such as flipped learning, particularly when these technologies are effectively integrated. In this context, the present study examined students’ perceptions of the effectiveness of AI-supported flipped learning in fostering cognitive, affective, and 21st-century competencies among English Education students at Sultan Qaboos University (SQU). A convergent mixed-methods design was used. Quantitative data were obtained using a five-point Likert-scale questionnaire administered to 60 undergraduate English Education students, while qualitative data were collected from students’ reflective journals. The findings revealed that students generally perceived AI-supported flipped learning as positively supporting cognitive outcomes, affective outcomes, and twenty-first-century competencies. Motivation and engagement emerged as the strongest affective outcomes, while students also reported positive perceptions of cognitive development, collaboration, digital competence, and self-regulated learning. Correlation analysis further demonstrated significant positive relationships among all six constructs, with the strongest associations observed between motivation and digital competence and between cognitive outcomes and both motivation and collaboration. Qualitative findings emphasized increased engagement, deeper learning experiences, and positive attitudes toward flipped learning. Despite minor challenges related to workload and time management, overall student perceptions were highly positive. The study concluded that students perceived AI-supported flipped learning as an effective pedagogical approach for fostering academic achievement and essential 21st-century skills in higher education.
R. Qassrawi, S. A. Al Karasneh· Education sciences· 0 citations
Artificial intelligence (AI) has transformed higher education by introducing intelligent learning environments that support personalized instruction, adaptive learning, automated assessment, and enhanced student engagement. While AI-supported education offers substantial academic benefits, its implications for students' mental health and psychological well-being remain insufficiently explored. Therefore, this study examined the relationship between AI-supported education and student mental health, This research aimed to identify artificial intelligence-supported education systems and teaching models and the effect that AI-assisted education has on students' psychological well-being and mental state. It focused on health in the field of higher education, balancing the innovations and psychological health of students. A quantitative, cross-sectional study was used. The primary data used were collected by means of a structured questionnaire. The targeted population for this study included both undergraduate and postgraduate students enrolled at public and private universities in Punjab and Sindh, Pakistan, including Bahauddin Zakariya University, The Women University Multan, University of the Punjab, Government College University Faisalabad, Islamia University of Bahawalpur, University of Sindh Jamshoro, Mehran University of Engineering and Technology, NED University of Engineering and Technology Karachi, Foundation university medical college, and the University of Karachi. The specific sample used for this study was convenience sampling. Google Forms were distributed to university mailing lists and academic social media platforms, collecting data from 457 participants. The data were screened using Microsoft Excel. The data were analyzed using IBM SPSS Statistics Version 27. Descriptive statistics and inferential statistics including Cronbach's Alpha, Pearson’s correlation, multiple linear regression, Independent Samples t-test, and One-Way ANOVA were employed for this study. The results showed that AI-supported education positively affected student mental health and psychological well-being, showing distinct demographic patterns for respondents. The results of this study build on established research to show that, when critically applied in the field of higher education, AI innovation may support the enhancement of not only teaching and learning but also the psychological well-being of students. Based on the results, recommendations are made for educators, higher education administrators, and policy makers, as well as instructional designers and education technology entrepreneurs, to aid the development of ethical, student-centered, and psychological support frameworks for the use of AI Learning technologies within the field of higher education.
Batool Butt, Nimra Jamil, Mahira Mirza et al.· Journal of Global Social Tra...· 0 citations
