Aug 2026· Journal of Global Social Transformation· Vol 2, pp. 267-283· 0 citations· 37 references
TL;DR
It is suggested that purposeful AI use may support students’ cognitive development, engagement with learning, and practical awareness of financial matters, and practical implications for higher education institutions seeking to promote AI literacy, critical evaluation, responsible AI use, and meaningful student development are provided.
Abstract
The rapid integration of artificial intelligence (AI) into higher education is reshaping how university students access information, engage with learning, and develop academic competencies. However, limited empirical research has examined AI-augmented cognition as a broader educational phenomenon connecting students’ cognitive use of AI with both academic and practical outcomes. This study examined the relationship of AI-augmented cognition with financial awareness, learning engagement, and students’ academic development among university students in Pakistan. A quantitative cross-sectional research design was adopted, and data were collected from 467 university students enrolled in higher education institutions across Punjab and Sindh, Pakistan. An adapted structured questionnaire containing established measures of AI-augmented cognition, financial awareness, learning engagement, and academic development was administered through Google Forms, and the survey link was distributed electronically to eligible respondents through academic and student networks. The collected responses were organized and screened before being analyzed quantitatively to examine relationships, predictive patterns, and differences among relevant student groups. The findings revealed that AI-augmented cognition was positively associated with financial awareness, learning engagement, and students’ academic development, with the strongest relationship observed with academic development. AI-augmented cognition also demonstrated a meaningful predictive contribution to all three outcomes, indicating that students who engaged more actively with AI as a cognitive resource tended to report greater financial awareness, stronger learning engagement, and enhanced academic development. Furthermore, students with previous generative AI experience demonstrated comparatively higher levels of the major study variables, while differences were also observed across varying levels of AI-use frequency. These findings highlight that the educational significance of AI extends beyond technological adoption and conventional academic assistance, suggesting that purposeful AI use may support students’ cognitive development, engagement with learning, and practical awareness of financial matters. The study contributes to the emerging literature by positioning AI as a cognitive augmentation resource rather than merely a digital learning tool and provides practical implications for higher education institutions seeking to promote AI literacy, critical evaluation, responsible AI use, and meaningful student development.
This study examined the relationships among artificial intelligence literacy, digital competence and academic engagement among Generation Z students enrolled in higher education institutions. A mixed-method research design was employed to obtain a comprehensive understanding of students’ preparedness for AI-supported learning. Quantitative data were collected through a structured questionnaire administered to undergraduate and postgraduate students from public and private universities. Qualitative data were obtained through semi-structured interviews with students and university faculty members to explore experiences, perceptions, and challenges associated with AI integration in higher education. Quantitative data were analysed using descriptive and inferential statistical techniques, while qualitative data were examined through thematic analysis. The integrated findings indicated that Generation Z students demonstrated varying levels of AI literacy and digital competence, which were significantly associated with their academic engagement. Students with stronger AI literacy and digital skills reported greater participation in learning activities, improved collaboration, enhanced problem-solving abilities and increased confidence in technology-supported learning environments. Qualitative findings further revealed that institutional support, faculty guidance, ethical awareness, and access to digital resources played critical roles in fostering meaningful academic engagement. Participants also emphasized the importance of responsible AI use, digital ethics and continuous skill development for effective learning in higher education. The study contributes to the growing body of knowledge on AI integration in education by providing empirical evidence that can inform curriculum development, institutional policy and professional practices aimed at preparing Generation Z learners for technology-rich academic environments.
Saira Saeed, Omar J. Alkhatib, Uzma et al.· TAMSAAL· 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
Findings show that the use of AImediates the relationship between DT and students’ behavioural and academic outcomes, and shows that the use of AI helps shape student experiences and outcomes.
N. Shaya, Rawan Abukhait, Muhammad Nisar Khattak et al.· Journal of Applied Learning...· 0 citations
AI has considerable potential to complement conventional pedagogical practices and contribute to more adaptive and student-centered higher education, provided that its implementation is guided by sound pedagogical principles and responsible governance frameworks.
Sugandha Sahay, Gouranga Patra· International Journal for Sc...· 0 citations
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
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