Despite the increased adoption of Artificial Intelligence in Education (AIED), several concerns are still associated with it. This has motivated researchers to conduct (systematic) reviews aiming at synthesizing the AIED findings in the literature. However, these AIED reviews are diversified in terms of focus, stakeholders, educational level and region, and so on. This has made the understanding of the overall landscape of AIED challenging. To address this research gap, this study proceeds one step forward by systematically meta-synthesizing the AIED literature reviews. Specifically, 143 literature reviews were included and analyzed according to the technology-based learning model. It is worth noting that most of the AIED research has been from China and the U.S. Additionally, when discussing AIED, strong focus was on higher education, where less attention is paid to special education. The results also reveal that AI is used mostly to support teachers and students in education with less focus on other educational stakeholders (e.g. school leaders or administrators). The study provides a possible roadmap for future research agenda on AIED, facilitating the implementation of effective and safe AIED.

While several studies have investigated the various effects of open educational resources (OER) and open educational practices (OEP), few have focused on its connection to learning achievement. The related scientific literature is divided about the effects of OER and OEP with regards to their contribution to learning achievement. To address this tension, a meta-analysis and research synthesis of 25 studies (N = 119,840 participants) was conducted to quantitatively investigate the effects of OER and OEP on students’ learning achievement. The analysis included course subject, level of education, intervention duration, sample size, geographical distribution, and research design as moderating variables of the obtained effects. The findings revealed that OER and OEP have a significant yet negligible (g = 0.07, p < 0.001) effect. Additionally, the analysis found that the obtained effect can be moderated by several variables, including course subject, level of education and geographical distribution. The study findings can help various stakeholders (e.g., educators, instructional designers or policy makers) in understanding what might hinder OER and OEP effect on learning achievement, hence accommodating better learning outcomes and more effective interventions.

Scant information exists about how AI with its different technologies might affect learning achievement in different educational fields across different educational levels and geographical distributions of students. Closing this gap can therefore help stakeholders understand under which learning conditions artificial intelligence in education (AIEd) might work or not, hence achieving better learning achievement. To address this research gap, this study conducted a meta-analysis and research synthesis of the effects of AI application on students’ learning achievement. Additionally, this study conducted one step forward to analyze the field of education, level of education, learning mode, intervention duration, and geographical distribution as moderating variables of the effect of AIEd. The Hedges’ g was computed for the effect sizes, where 85 quantitative studies ( N = 10,469 participants) were coded and analyzed. The results indicated that the total effect of AIEd on learning achievement is very large ( g = 1.10, p < 0.001). Particularly, chatbots achieved a very large effect, while Intelligent Tutoring Systems (ITS) and personalized learning systems had large effects. The results also show that the AIEd effect is moderated by the field of education, level of education, learning mode, intervention duration, and geographical distribution of students. The findings of this study can be useful to both researchers and practitioners as they highlight how and when AIEd integration can be effective, hence being beneficial to enhance learning achievement.

It is widely acknowledged that the relationship between education, security, and stability is bidirectional. In times of war, access to quality education becomes compromised, and conversely, the absence of proper education constrains individuals to a life under constant threat, disrupting education provision. War, viewed as a “lifetime and life-wide status,” evolves into a self-fulfilling prophecy, both at an individual and collective level, making it challenging to break free from, particularly within the broader context of education provision. Although, scant information exists about how education is maintained during wars, hindering the right to education in such contexts. This study therefore addresses this gap by synthesizing the literature to identify and present 14 educational scenarios and teaching strategies on how education was maintained from different war contexts over time. The findings reveal that education during wars can be digital-based (i.e., facilitated by technology) and non-digital based (without technology). Additionally, various teaching strategies are applied during wars, including inspirational, hands-on and practical, fun-based, among others. Finally, teaching during wars is not limited to teachers only, but it could also involve parents, neighbors, etc. The findings of the literature can help to ensure the right to education in crises like wars and reveal the importance of open education in such crises. They can also contribute to enriching the ongoing theoretical and practical debate on how to maintain education in crises like wars. This can help to better be prepared for future education in crises, which is the focus of several international organizations.