There is an increasing interest and effort in reorienting university curricula to address sustainability and preparing teachers to get involved in embedding sustainability and the UN’s Sustainable Development Goals (SDGs) in teaching and curricula enabled by Information and Communication Technologies (ICTs). Evidence shows that this interest and effort is often prevented by various barriers at three levels: teacher- level barriers, school-level barriers, and system-level barriers. In this study, the attempt was geared towards identifying the constituencies of these three levels of barriers and examining the extent to which they predict teachers’ motivation to embed sustainability and SDGs in various school subject areas, including arts-based education. A survey of 1253 teachers in Malaysia revealed that the teacher- and system-level barriers explain 83% of the motivation variance. By identifying, addressing, and investigating these barriers, higher education institutions—and especially teacher education—could be better informed in reorienting university curricula to embed ICT-enabled Education for Sustainability (ICTeEfS). These results were also used in planning and implementing in-service teacher training interventions in the context of a European Commission Erasmus+-funded project.

HRMARS is well established journal publisher based in Pakistan. It is publishing prominent journals in social sciences since 2011.

The integration of Artificial Intelligence (AI) technologies, particularly generative AI, has revolutionized online pedagogy by offering personalized learning experiences, fostering creativity, and enhancing educational efficiency. However, understanding the applications and implications of these technologies in education remains fragmented. This Systematic Literature Review (SLR) addresses this gap by synthesizing research on the role, benefits, challenges, and future directions of generative AI in teaching and learning environments. Guided by the PICo framework, the study employed a rigorous methodology. A comprehensive search strategy identified relevant peer-reviewed literature published between 2013 and 2025. The PRISMA 2020 flow diagram ensured a transparent selection process, with 260 studies included after applying inclusion and exclusion criteria. The findings reveal that generative AI significantly enhances educational practices by automating routine tasks, personalizing instruction, and enabling innovative teaching methods. However, ethical concerns, such as data privacy and algorithmic bias, technical challenges, including dataset limitations and computational inefficiencies, and pedagogical barriers, like over-reliance on AI and resistance from educators, present substantial hurdles. Future directions emphasize the development of ethical frameworks, AI literacy programs for educators, and policies promoting equitable access to technology. This review contributes to the academic discourse by providing actionable insights for educators, policymakers, and researchers, underscoring the importance of addressing these challenges to fully realize AI’s transformative potential in education.