Artificial intelligence (AI) has been widely explored across the world over the past decades. A particularly emerging topic is the application of AI in e-learning (AIeL) to improve the effectiveness of teaching and learning in precision education. This study aims to systematically review publication patterns for AIeL research with a focus on leading journals, countries, disciplines, and applications. In addition, a co-citation network analysis was conducted to explore the invisible relationships among the core papers of AIeL to reveal directions for future research. The analysis is based on a total of 86 core AIeL papers accompanied by 1149 citations in follow-up studies obtained from the Web of Science. It was found that a majority of AIeL studies focused on the development and applications of intelligent tutoring systems, followed by using AI to facilitate assessment and evaluation in e-learning contexts. For field researchers, the visualized network diagram serves as a map to explore the invisible relationships among the core AIeL research, providing a structural understanding of AI-supported research in e-learning contexts. A further investigation of the follow-up studies behind the highly co-cited links revealed the extended research directions from the AIeL mainstreams, such as adaptive learning-based evaluation environments. Implications are discussed.

This study explored the transformative potential of generative artificial intelligence (GAI) for achieving the UN Sustainable Development Goal on Quality Education (SDG4), emphasizing its interconnectedness with the other SDGs. A proprietary algorithm and cocitation network analysis were used to identify and analyze the network of SDG features in GAI research publications (n = 1501). By examining GAI’s implications for ten SDG4 targets, the findings advocate for a collaborative, ethical approach to integrating GAI, emphasizing policy and practice developments that ensure that technological advancements align with the overarching goals of SDG4. The results highlight the multifaceted impact of GAI on the SDGs. First, this paper outlines a framework that leverages GAI to enhance educational equity, quality, and lifelong learning opportunities. By highlighting the synergy between GAI and the SDGs, such as reducing inequalities (SDG10) and promoting gender equality (SDG5), this study underscores the need for an integrated approach to utilizing GAI. Moreover, it advocates for personalized learning, equitable technology access, adherence to ethical AI principles, and fostering global citizenship, proposing a strategic alignment of GAI applications with the broader SDG agenda. Next, the results highlight that GAI introduces significant challenges, including ethical concerns, data privacy, and the risk of exacerbating the digital divide. Overall, our findings underscore the critical role of policy reforms and innovative practices in navigating the challenges and harnessing the opportunities presented by GAI in education, thereby contributing to a comprehensive discourse on technology’s role in advancing global education and sustainable development.

This study provides research-based evidence to profile: (1) the roles of artificial intelligence in nursing, (2) its research applications, and (3) the research trends for future study. On the basis of the PRISMA statement, a series of AI and nursing education related keywords from the literature were used to retrieve high-quality journal articles from the Web of Science. A total of 112 AI-supported nursing education research articles were analyzed based on a three-dimensional framework, including interaction (e.g. the roles of AI, types of AI systems), research (e.g. methods and fields), and performance (e.g. research groups and measurement foci). The results revealed that AI played a primary role in profiling and prediction in nursing research (63%), and the most used AI system in nursing was intelligent agents (53%). The quantitative approach (87%) was the dominant research method, and the most relevant studies concerned health and medicine (92%). Regarding sample and measurement matters, patients and medical staff (75%) were the two primary research samples, and the performance evaluation of AI-related tools and systems (90%) was the core measurement focus. Additional content analysis across the three research interests was performed and discussed. Directions for future studies are provided.