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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.

The purpose of this study is to analyze from multiple perspectives, so as to form an effective massive open online course (MOOC)personalized recommendation method to help learners efficiently obtain MOOC resources.

This study introduced ontology construction technology and a new semantic association algorithm to form a new MOOC resource personalized recommendation idea. On the one hand, by constructing a learner model and a MOOC resource ontology model, based on the learner’s characteristics, the learner’s MOOC resource learning preference is predicted, and a recommendation list is formed. On the other hand, the semantic association algorithm is used to calculate the correlation between the MOOC resources to be recommended and the learners’ rated resources and predict the learner’s learning preferences to form a recommendation list. Finally, the two recommendation lists were comprehensively analyzed to form the final MOOC resource personalized recommendation list.

The semantic association algorithm based on hierarchical correlation analysis and attribute correlation analysis introduced in this study can effectively analyze the semantic similarity between MOOC resources. The hybrid recommendation method that introduces ontology construction technology and performs semantic association analysis can effectively realize the personalized recommendation of MOOC resources.

This study has formed an effective method for personalized recommendation of MOOC resources, solved the problems existing in the personalized recommendation that is, the recommendation relies on the learner’s rating of the resource, the recommendation is specialized, and the knowledge structure of the recommended resource is static, and provides a new idea for connecting MOOC learners and resources.

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.

As educators seek ways to enhance student motivation and improve achievement, promising advances are being made in adaptive approaches to instruction. Learning technologies are emerging that promote a high level of personalization of the learning experience. One type of personalization is context personalization, in which instruction is presented in the context of learners’ individual interests in areas like sports, music, and video games. Personalized contexts may elicit situational interest, which can in turn spur motivational and metacognitive states like positive affect and focused attention. Personalized contexts may also allow for concepts to become grounded in prior knowledge by fostering connections to everyday activity. In this Chapter, we discuss the theoretical, design, and implementation issues to consider when creating interventions that utilize context personalization to enhance motivation.

First, we provide an overview of context personalization as an instructional principle and outline the emerging evidence that personalization can enhance motivation and improve achievement. We then discuss the theory hypothesized to account for the effectiveness of context personalization and discuss the approaches to personalization interventions. We close by discussing some of the practical issues to consider when bridging the design and implementation of personalization interventions. Throughout the paper, we anchor our discussion to our own research which focuses on the use of context personalization in middle and high school mathematics.

The theoretical mechanisms through which context personalization enhances learning may include (1) eliciting positive affective reactions to the instruction, (2) fostering feelings of value for the instructional content through connections to valued personal interests, or (3) drawing upon prior funds of knowledge of the topic. We provide hypotheses for the relatedness of context personalization to triggering and maintaining situational interest, and explore potential drawbacks of personalization, considering research on seductive details, desirable difficulties, and authenticity of connections to prior knowledge. We further examine four approaches to personalized learning – “fill-in-the-blank” personalization, matching instruction to individual topic interests, group-level personalization, and utility-value interventions. These approaches vary in terms of the depth of the personalization – whether simple, shallow connections are made to interest topics, or deep, meaningful connections are made to learners’ actual experiences. The consideration of depth also interacts with grain size – whether content is personalized based on the broader interests of a group, or the individual experiences of a particular learner. And finally, personalization interventions can have different levels of ownership – an instructor can generate the personalized connections, the connections can be made by the curriculum designers, or learners can take an active role in personalizing their own learning. Finally, we discuss the practical implementation issues when bringing context personalization interventions into K-12 classrooms. Personalization can be logistically difficult to implement, given that learners hold a diverse array of interests, and may experience each of those interests differently. In addition, particular types of instructional content may show greater sensitivity when personalization is implemented, and personalization may be most helpful for learners with certain background characteristics.

Realizing the promise of personalized learning is an unsolved problem in education whose solution becomes ever more critical as we confront a new digital age. Context personalization has the potential to bring together several well-established strands of research on improving student learning – research on the development of interest, funds of knowledge, and utility value – into one powerful intervention.

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.

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.

The purpose of this paper is to explore what is believed to be a deep connection between Islamic pedagogy as a way to cultivate personal learning experiences. The paper discusses the relationship between the characterising features of Islamic pedagogy and personalised learning that remains central to Islamic institutional developments. The paper concludes by highlighting the importance of the embodiment of knowledge in Islamic pedagogy for personalised learning.

The endeavours to define the characterising features that represents the relationship between Islamic pedagogy and knowledge embodiment.

The paper proposes that Islamic pedagogy is dependent on both a personalised approach towards teacher and student embodiment. From an Islamic perspective, embodiment has a physical and spiritual dimension where prophecy is retained and is inherent to existence and daily practice. Without the embodied learning the Islamic approach towards pedagogy is seen to disconnect with many students seeking knowledge. This highlights the centrality of the teachers’ relationship with the student and the distinguishing belief of Islamic pedagogy in knowledge embodiment.

The papers contribution to knowledge is in considering personalised learning within the context of Islamic education.

Recent research in higher education has pointed out that personalized e-learning through the use of learning management systems, such as Moodle, improves the academic results of students and facilitates the detection of at-risk students.

A sample of 124 students following the Degree in Health Sciences at the University of Burgos participated in this study. The objectives were as follows: to verify whether the use of a Moodle-based personalized e-learning system will predict the learning outcomes of students and the use of effective learning behaviour patterns and to study whether it will increase student satisfaction with teaching practice.

The use of a Moodle-based personalized e-learning system that included problem-based learning (PBL) methodology predicted the learning outcomes by 42.3 per cent, especially with regard to the results of the quizzes. In addition, it predicted effective behavioural patterns by 74.2 per cent. Increased student satisfaction levels were also identified through the conceptual feedback provided by the teacher, arguably because it facilitated a deeper understanding of the subject matter.

The results of this work should be treated with caution, because of the sample size and the specificity of the branch of knowledge of the students, as well as the design type. Future studies will be directed at increasing the size of the sample and the diversity of the qualifications.

Learning methodology in the twenty-first century has to be guided towards carefully structured work from the pedagogic point of view in the learning management systems allowing for process-oriented feedback and PBL both included in personalized e-learning systems.

This chapter reviews the recent developments at the intersection of personalization and AI in marketing and related fields. We provide a formal definition of personalized policy and review the methodological approaches available for personalization. We discuss scalability, generalizability, and counterfactual validity issues and briefly touch upon advanced methods for online/interactive/dynamic settings. We then summarize the three evaluation approaches for static policies – the Direct method, the Inverse Propensity Score (IPS) estimator, and the Doubly Robust (DR) method. Next, we present a summary of the evaluation approaches for special cases such as continuous actions and dynamic settings. We then summarize the findings on the returns to personalization across various domains, including content recommendation, advertising, and promotions. Next, we discuss the work on the intersection between personalization and welfare. We focus on four of these welfare notions that have been studied in the literature: (1) search costs, (2) privacy, (3) fairness, and (4) polarization. We conclude with a discussion of the remaining challenges and some directions for future research.