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This paper aims to present a comprehensive overview of the patterns and trends of publications on artificial intelligence (AI) in personalised learning. It addresses the need to investigate the intellectual structure and development of this area in view of the growing amount of related research and practices.

A bibliometric analysis was conducted to cover publications on AI in personalised learning published from 2000 to 2022, including a total of 1,005 publications collected from the Web of Science and Scopus. The patterns and trends in terms of sources of publications, intellectual structure and major topics were analysed.

Research on AI in personalised learning has been widely published in various sources. The intellectual bases of related work were mostly on studies on the application of AI technologies in education and personalised learning. The relevant research covered mainly AI technologies and techniques, as well as the design and development of AI systems to support personalised learning. The emerging topics have addressed areas such as big data, learning analytics and deep learning.

This study depicted the research hotspots of personalisation in learning with the support of AI and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and the need for future work on diverse means to support personalised learning with AI, the pedagogical issues, as well as teachers’ roles and teaching strategies.

This paper aims to present a comprehensive review of the present state and trends of smart education research. It addresses the need to have a systematic review of smart education to depict its research landscape in view of the growing volume of related publications.

A bibliometric analysis of publications on smart education published in 2011 to 2020 was conducted, covering their patterns and trends in terms of collaboration, key publications, major topics and trends. A total of 1,317 publications with 29,317 cited references were collected from the Web of Science and Scopus for the bibliometric analysis.

Research on smart education has been widely published in various sources. The most frequently cited references are all theoretical or discussion articles. Researchers in the USA, China, South Korea, India and Russia have been most active in research collaborations. However, international collaborations have remained infrequent except for those involving the USA. The research on smart education broadly covered smart technologies as well as teaching and learning. The emerging topics have addressed areas such as the Internet of Things, big data, flipped learning and gamification.

This study depicts the intellectual landscape of smart education research, and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and research needs, and suggest future work related to research collaborations on a larger scale and more studies on smart pedagogies.

This paper aims to present a review of case studies on the use of learning analytics in Science, Technology, Engineering, (Arts), and Mathematics (or STE[A]M) education. It covers the features and trends of learning analytics practices as revealed in case studies.

A total of 34 case studies published from 2013 to 2018 reporting relevant learning analytics practices were collected from Scopus and Google Scholar for analysis. The features and trends of practices were identified through a comparison of the first (2013–2015) and the second period (2016–2018).

The results showed an increasing adoption of learning analytics in STE(A)M education, particularly in the USA and European countries and at the tertiary level. More specific types of data have been collected for the learning analytics practices, and the data related to students’ learning processes has also been more frequently used. The types of STE(A)M learning practices have become more diversified, with technology enhancement features increasingly introduced. The outcomes of the case studies reflect the overall benefits of learning analytics and address the specific needs of STE(A)M education. There have also been fewer types of limitations encountered in the learning analytics practices over the years, with unknown correlation among variables, small sample size and limited data being the major types.

This study reveals the implementation of learning analytics in relation to the contexts and needs of STE(A)M education. The findings also suggest future work for examining the adoption of learning analytics to cope with the development of STE(A)M and, in particular, how the successful experience of learning analytics in other disciplines could be transferred to STE(A)M.

The purpose of this paper is to evaluate the implementation of mobile learning in a nursing course at The Open University of Hong Kong, and identify the potentials of, and constraints on, introducing mobile technologies in the instructional design of nursing education. The paper also considers the pedagogical implications of the expansion of mobile learning in the field of nursing.

The research adopts a qualitative approach to obtain the students’ and teacher’s experiences, opinions, and expectations on mobile learning. Two focus groups with 20 student participants were conducted and an in-depth interview with the course teacher was arranged. The Framework for the Rational Analysis of Mobile Education (FRAME) model was used as the research framework to support data collection and analysis.

The aspects of device usability, interaction learning, and social technology as suggested in the FRAME model were partly fulfilled in the study. Mobile technology enhanced the portability and accessibility of learning information, and networking tools facilitated interaction among students and between students and the teacher. However, the readability of text was limited due to constraints on the user interface and screen size, and concerns over the reliability of learning content were also raised, given the abundance of unfiltered online information. The difficulty in updating the content of multimedia materials and sourcing videos of an appropriate level, together with the problem of device networking, also limited the usefulness of mobile learning. Attention should also be paid to the perceptual differences between students and the teacher on the nature and functions of mobile learning.

This empirical study provides a detailed evaluation of the delivery of mobile learning in a nursing course. The findings reveal the strengths and limitations of using mobile technologies to support the nursing education.