Jul 2026· Journal of Applied Learning and Teaching· Vol 9· 0 citations
TL;DR
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.
Abstract
Although blended learning (BL) represents a transformative educational model, it is unclear how digital transformation (DT) shapes the dynamics of BL, particularly through the integration of artificial intelligence (AI), in higher education. This study examines how artificial intelligence–supported self-regulated learning (AI-SRL), informed by cognitive load theory and AI-driven pedagogy, can enhance BL models to promote equitable, inclusive education and advance sustainable development within the context of ongoing DT. Three hundred and ten higher-education students in the United Arab Emirates (UAE) who participated in BL courses completed an online survey. In addition, 11 faculty members and academic leaders were interviewed. The findings show that the use of AI mediates the relationship between DT and students’ behavioural and academic outcomes – namely, procrastination, heuristic processing, and academic achievement. DT increases the adoption of AI to improve academic performance while simultaneously increasing procrastination and reliance on heuristic (surface-level) processing. AI-SRL mitigates these negative outcomes, reducing procrastination and heuristic processing. Furthermore, AI-integrated pedagogy empowers students to harness the benefits of AI-enhanced DT to improve academic performance while minimising adverse cognitive and behavioural effects. This study provides a nuanced understanding of BL in higher education, showing that the use of AI helps shape student experiences and outcomes.
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
Findings reveal that the teacher's engagement is non-linear and front-loaded: while Behavioral Engagement peaked during the Design and Development phases through active material generation, it significantly declined during the Implementation phase due to infrastructural anxiety and instability.
Nasai Danzeng· Region - Educational Researc...· 0 citations
The study explores the paradigm shift in education brought about by the introduction of generative artificial intelligence (AI) tools, focusing on educational stakeholders’ self-reported perceptions rather than observed changes in teaching or learning outcomes. We consider stakeholders’ views on AI-based technologies within the teaching–learning process. The current study uses a cross-sectional empirical survey design with a sample of N = 917 respondents, including teachers, students, administrators, and management. It examines the use of advanced AI technologies such as ChatGPT, Gemini, DeepSeek, and Grok, and stakeholders’ perceived connection between digital skills and classroom performance, student motivation, and critical thinking. We also discuss the ethical dilemmas and structural challenges that accompany this digital change. Inferential statistics, such as One-Way ANOVA and the Pearson Chi-Square test, show statistically significant differences in perceptions and regulatory expectations across organizational responsibilities. The findings contribute to understanding how advanced digitalization is perceived to reshape traditional academic roles, offering practical insights for creating effective, responsible, and sustainable teaching practices.
Abstrak: The transformation of 21st-century education demands learning that accommodates differences in learning styles, adaptive needs, and improves student learning outcomes. The use of Artificial Intelligence (AI) and gamification in learning media offers an innovative approach that not only increases learning motivation but also strengthens critical and collaborative thinking processes. This study aims to analyze how the development of AI-based learning media and gamification contributes to improving student learning outcomes. The research method used a Systematic Literature Review (SLR) of 20 indexed scientific articles from 2022–2025 through the Consensus platform. The results show that AI integration supports learning personalization and automated evaluation, while gamification creates an interactive and challenging learning environment. Empirically, the combination of the two improves student learning outcomes by an average of 35–45% compared to conventional methods. These results emphasize the importance of collaboration between technological innovation and adaptive pedagogy to build an inclusive and sustainable digital education ecosystem.
Moh. Mazhud Hasyim, Moh. Dafid, M.Ali Zainal Abidin et al.· Journal on Research and Revi...· 0 citations
A suppression effect is revealed in which AIG promotes inclusive learning outcomes exclusively through engagement, motivation, and personalization pathways, revealing a suppression effect in which AIG promotes inclusive learning outcomes exclusively through engagement, motivation, and personalization pathways.
Prashanth Beleya, N. Chauhan, K. P. Jaheer Mukthar et al.· Frontiers of Computer Scienc...· 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
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