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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 chapter will explore how the places of learning might look in next generation learning spaces where learners traverse physical and virtual spaces using personalised learning strategies. It will examine how learning spaces may represent ubiquitous spaces in which the learner undertakes some form of study or learning. Although there has been extensive examination of the design of spaces for knowledge generation (Keppell & Riddle, 2012, 2013; Souter, Riddle, Sellers, & Keppell, 2011) there has been little attention given to how learners customise and personalise their own physical and virtual learning spaces as they traverse their learning journey. Seven principles of learning space design will be adapted for use by the personalised learner. Personalised learning strategies encompass a range of knowledge, skills and attitudes that empower the learner to take charge of their learning within next generation learning spaces. Personalised learning consists of six broad concepts: digital citizenship, seamless learning, learner engagement, learning-oriented assessment, lifelong and life-wide learning and desire paths. Teachers will need to assist learners to design their own personalised learning spaces throughout formal education to encourage learners to be autonomous learners throughout their lifetime. In order to assist learners in developing personalised learning strategies we need to teach them about learning space literacies. We can’t assume learners have the knowledge, skills and attitudes to be able to identify and effectively utilise appropriate learning spaces that optimises engagement.

The purpose of this study is to build a personalized learning intervention system, which can support students' personalized learning, improve teachers' teaching efficiency and students' learning effect.

The research proposes a personalized learning intervention method based on a collaborative filtering algorithm and knowledge map. The application of knowledge map makes learning content organized, and the use of collaborative filtering algorithm makes it possible to provide personalized learning recommendations for students. This personalized learning intervention system can monitor students' learning development and achieve the combination of personalized and efficiency. For the study, 152 seventh graders were assigned to a control group and an experimental group. Traditional learning intervention was used in the control group, and individualized learning intervention was used in the experimental group.

SPSS was used for data organization and analysis. The effectiveness of the personalized learning intervention system is verified by quasi-experimental research, and the influence of the system on students' learning effect is discussed. The result found that personalized learning interventions were more effective than traditional approaches in improving students’ achievement. However, for students of different learning levels, personalized learning intervention system has different effects on learning confidence and learning anxiety.

The personalized learning intervention system based on the collaborative filtering algorithm and knowledge map is effective in improving learning effect. And, it also has a certain influence on students' psychology.

This chapter explores children’s agency in using mobile technologies at home and in school. Supporting children’s agency has been offered as a rationale for adopting personalised education worldwide. Children’s agency is also drawn upon as a justification for children’s use of personal mobile devices. This chapter considers children’s agency in light of the personalised education in one UK primary school and the children’s use of mobile technologies at school and at home. The findings are based on eight days of observations of classroom practice and interviews with six case study children in the Year 6 classroom. In sessions that were supported with mobile technologies, children’s learning was personalised to each child, but constrained by the amount of time that the activity lasted and that the technology was available for. Based on children’s accounts, their use of mobile technologies at home was constrained by their parents’ restrictions and monitoring practices. The chapter discusses the reality of children’s agency in light of adults’ mediation and children’s actual experiences of personalised learning.

Education 4.0 (E 4.0) represents a new paradigm in the field of education, which emphasizes a student-centric approach that allows learners to access education anytime, anywhere, tailored to their individual needs through modern-day technologies. The purpose of the study was to unearth the critical success factors (CSFs) essential for the successful implementation of E 4.0.

The CSFs were unearthed using a literature review and further the interrelationships were analysed using multi-criteria decision making (MCDM) approach.

The study unearthed 15 CSFs for the successful implementation of E 4.0. The most important factor for the successful implementation of E 4.0 was personalized learning which was found to be the casual factor. The other causal CSFs were clear vision and leadership for E 4.0, stakeholder involvement, data analytics in teaching and learning, inter-disciplinary learning and blended learning environments. The effect factors were digital citizenship-based education, teacher training and development for E 4.0, supportive environment, curriculum redesign for E 4.0, open educational resources, digital technologies, formative assessments, infrastructure for E 4.0 and sustainability in education.

This is the first study which unearthed the CSFs and found the interrelationships among them, thus contributing to the theory of technology organization environment.

This study represented a pioneering effort in understanding the CSFs underpinning the successful adoption of E 4.0, paving the way for a more personalized, tech-savvy and effective education system.

This study investigates the impact of large-scale teaching in higher education on students’ preparedness for the workforce within the context of evolving labour market demands, the expansion of higher education and the application of high-impact teaching strategies. It synthesizes perspectives on employer work readiness, the challenges and opportunities of large-scale teaching and strategies for fostering a dynamic academia-industry feedback loop. This multifaceted approach ensures the relevance of curricula and graduates’ preparedness while addressing the skills gap through practical recommendations for aligning teaching methodologies with employer expectations.

The research methodically examines the multifaceted challenges and opportunities inherent in large-scale teaching. It focuses on sustaining student engagement, maintaining educational quality, personalizing learning experiences and cultivating essential soft skills in extensive student cohorts.

This study highlights the critical role of transversal skills in work readiness. It also uncovers that despite its challenges, large-scale teaching presents unique opportunities. The diversity of large student groups mirrors modern workplace complexities, and technological tools aid in personalizing learning experiences. Approaches like peer networking, innovative teaching methods, real-world simulations and collaborative resource utilization enrich education. The importance of experiential learning for augmenting large-scale teaching in honing soft skills is emphasized.

This manuscript contributes to the discourse on large-scale teaching, aligning it with employer expectations and the dynamic requirements of the job market. It offers a nuanced perspective on the challenges and opportunities this educational approach presents, providing insights for crafting engaging and effective learning experiences in large cohorts. The study uniquely integrates experiential learning, co-creation in education and industry-academia feedback loops, underscoring their importance in enhancing student work readiness in large-scale teaching.

This paper aims to look at the potential impact of personalized or competency-based learning on educational institutions at the structural level and in the knowledge acquisition domain.

This article takes the structure of an editorial piece.

The essay suggests that the shift in pedagogy can impact on the operational and economic structure of the institution separate from the educational impact.

The potential impact of pedagogy shift could affect the economics of institutions as well as the entire operational structure and employment of the institution at the administrational level.