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Personalised learning (PL) is learning in which the stage of learning and the instructional approach are optimised for the needs of each learner. The concept of PL allows e-learning design to shift from a ‘one size fits all’ approach to an adaptive and student-centred approach. This paper aims to provide a literature review of PL based on: the PL components used to analyse learner diversity, the PL features offered, the methods used in developing the PL model, the resulting model, the learning theories applied and the impact of PL implementation. Thirty-nine out of 1654 articles published between 2017 and 2021 which were found by Kitchenham method were studied and analysed. The results are derived from synthesized through qualitative synthesis using thematic analysis. The results reveal that most of the articles used knowledge level and learner characteristics to analyse learner diversity. The teaching materials and learning path were the most widely offered PL features in PL model. There is a trend in determining PL features using the knowledge graph method and the use of machine learning classification algorithms to analyse learner diversity. The results also show that PL implementation improves learning outcomes and increases learner’s satisfaction, motivation, and engagement. Research analysing the impact of PL implementation on learning is limited. In addition, only a few studies explicitly referred to learning theory in relation to PL model development. Further research topics are suggested.

This paper reviews the literature on integrating AI in e-learning, from the viewpoint of cognitive neuropsychology, for Personalized Learning (PL) and Adaptive Assessment (AA). This review follows the PRISMA systematic review methodology and synthesizes the results of 85 studies that were selected from an initial pool of 818 records across several databases. The results indicate that AI can improve students’ performance, engagement, and motivation; at the same time, some challenges like bias and discrimination should be noted. The review covers the historic development of AI in education, its theoretical grounding, and its practical applications within PL and AA with high promise and ethical issues of AI-powered educational systems. Future directions are empirical validation of effectiveness and equity, development of algorithms that reduce bias, and exploration of ethical implications regarding data privacy. The review identifies the transformative potential of AI in developing personalized and adaptive learning (AL) environments, thus, it advocates continued development and exploration as a means to improve educational outcomes.

Artificial intelligence is the driving force of change focusing on the needs and demands of the student. The research explores Artificial Intelligence in Education (AIEd) for building personalised learning systems for students. The research investigates and proposes a framework for AIEd: social networking sites and chatbots, expert systems for education, intelligent mentors and agents, machine learning, personalised educational systems and virtual educational environments. These technologies help educators to develop and introduce personalised approaches to master new knowledge and develop professional competencies. The research presents a case study of AIEd implementation in education. The scholars conducted the experiment in educational establishments using artificial intelligence in the curriculum. The scholars surveyed 184 second-year students of the Institute of Pedagogy and Psychology at the Abay Kazakh National Pedagogical University and the Kuban State Technological University to collect the data. The scholars considered the collective group discussions regarding the application of artificial intelligence in education to improve the effectiveness of learning. The research identified key advantages to creating personalised learning pathways such as access to training in 24/7 mode, training in virtual contexts, adaptation of educational content to personal needs of students, real-time and regular feedback, improvements in the educational process and mental stimulations. The proposed education paradigm reflects the increasing role of artificial intelligence in socio-economic life, the social and ethical concerns artificial intelligence may pose to humanity and its role in the digitalisation of education. The current article may be used as a theoretical framework for many educational institutions planning to exploit the capabilities of artificial intelligence in their adaptation to personalized learning.

In personalized learning, each student gets a customized learning plan according to their pace of learning, instructional preferences, learning objects, etc. Hence the content recommender system in Personalized Learning Environment (PLE) should adapt to learner attributes and suggest appropriate learning resources to aid the learning process and improve the learning outcomes. This systematic literature review aims to analyze and summarize the studies on learning content recommenders in adaptive and personalized learning environments from 2015 to 2020. The publications were searched using proper keywords and filtered using the inclusion and exclusion criteria, which resulted in 52 publications. This paper summarizes the recent trends in research on different aspects of the recommender systems, such as learner attributes, recommendation methods, evaluation metrics, and the usability tests used by the researchers. It is observed that cognitive aspects of learners like learning style, preferences, knowledge level, etc., are used by most studies than non-cognitive aspects as social tags or trust. In most cases, recommendation engines are a hybrid of collaborative filtering, content-based filtering, ontological approaches, etc. All models were evaluated for the correctness of the prediction done, and a few studies have also done evaluations based on learner satisfaction or usability.

With the rapid development of online education, personalized learning path recommendations have played an increasingly important role in enhancing learning efficiency and optimizing learning experiences. However, existing learning path recommendation methods still face significant limitations in knowledge structure modeling, dynamic learner knowledge state perception, and recommendation strategy optimization. To address these challenges, this study proposes an online personalized English learning path recommendation method that integrates a domain knowledge graph with deep reinforcement learning. The graph encodes prerequisite (directed) and semantic (undirected) relations and uses a resource-to-knowledge mapping to structurally bind learning resources to concepts; learner mastery is updated in real time via interaction feedback, graph-based propagation, and an exponential forgetting mechanism. The task is formulated as an MDP in which Q-learning provides value-based pruning of prerequisite-feasible candidates and PPO selects the final action from the pruned set (a prune–then–select workflow). Deployed as a WeChat Mini Program, the system was evaluated on  200 active learners over three months with 18,742 valid interactions. It achieves Precision 0.85, Recall 0.82, F1 0.84, MAE 0.12, RMSE 0.18, cumulative return G 650, and AMG 0.42, consistently outperforming strong baselines AKT, LightGCN, TA-RL, cDQN, KG-H, MC, CF, and Rule; paired per-learner tests with BH–FDR control confirm significance, particularly for Top-K [3,10]. Engineering evaluations show an average  241 ms latency for personalized recommendation at 200 concurrent threads and sub-350 ms / sub-500 ms startup on Wi-Fi / 4G across mainstream devices, demonstrating practical scalability and real-time applicability.

This research aims to examine how artificial intelligence (AI) can be used within the educational framework for developing personalized learning paths. In order to achieve this goal, an etic approach is employed, and a qualitative-quantitative perspective is adopted. Thus, following the PRISMA guidelines, 71 articles published on Web od Science, during January 2014 – June 2024, are selected and analysed using cluster and density analysis. The results bring forward that the peak of the scientific production was reached in 2022 and that the topic is more appealing to the scholars from the information technology field than to the ones from the educational area. Furthermore, two lines of research can be identified; one that is technology-driven and another one that is learner/human-driven. Further research is required in providing a nexus between the two of them since, in the context of Industry 5.0 and Society 5.0, AI could act as a bridge. This research has several implications. On the one hand, it emphasizes the topics that captured scholars’ attention and also various research gaps that should be addressed. On the other hand, it extends the research from the educational management area by highlighting how AI could facilitate the transition towards the implementation of the connectivism learning theories.

This paper presents a comprehensive systematic review of personalized learning software systems. All the systems under review are designed to aid educational stakeholders by personalizing one or more facets of the learning process. This is achieved by exploring and analyzing the common architectural attributes among personalized learning software systems. A literature-driven taxonomy is recognized and built to categorize and analyze the reviewed literature. Relevant papers are filtered to produce a final set of full systems to be reviewed and analyzed. In this meta-review, a set of 72 selected personalized learning software systems have been reviewed and categorized based on the proposed personalized learning taxonomy. The proposed taxonomy outlines the three main architectural components of any personalized learning software system: learning environment, learner model, and content. It further defines the different realizations and attributions of each component. Surveyed systems have been analyzed under the proposed taxonomy according to their architectural components, usage, strengths, and weaknesses. Then, the role of these systems in the development of the field of personalized learning systems is discussed. This review sheds light on the field’s current challenges that need to be resolved in the upcoming years.

Learning is a natural human activity that is shaped by personal experiences, cognitive awareness, personal bias, opinions, cultural background, and environment. Learning has been defined as a stable and persistent change in what a person knows and can do. Learning is formed through an individual’s interactions, including the conveyance of knowledge and skills from others and experiences. So, learning is a personalized experience that allows one to expand their knowledge, perspective, skills, and understanding. Therefore, personalized learning models can help to meet individual needs and goals. Furthermore, to personalize the learning experience, technology integration can play a crucial role. This paper provides a review of the recent research literature on personalized learning as technology is changing how learning can be effectively personalized. The emphasis is on the terms used to characterize learning as those can suggest a framework for personalized and will eventually be used in meta-analyses of research on personalized learning, which is beyond the scope of this paper.

PurposeThis 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.Design/methodology/approachA 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.FindingsResearch 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.Originality/valueThis 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 process of teaching and learning during the pandemic has been evolving globally, with many institutions transforming their approaches to enhance the teaching and learning experience. Despite the presence of improved frameworks due to the varied learning capabilities of students, it remains quite challenging to analyse individual characteristic features. Consequently, this research provides clear insights into the integration of the Personalised Learning Approach (PLA) to foster effective interaction with students. However, many existing methods suggest different techniques for evaluating learners in a hybrid mode, where obtaining clear data sets can be difficult. In the teaching and learning approach, if the defined data set from experts is clear, decisions regarding the learning characteristics of students can be made in a shorter period. In the proposed method the PLA framework categorizes learners into four engagement-based clusters using a three-dimensional sensor model and machine learning classifiers. A dual-controller mechanism (master-slave) dynamically adjusts communication intervals and optimizes video transmission, reducing latency and packet loss. The methodology is validated using MATLAB-based simulations with a dataset of 1,700–5,000 learners, analyzing throughput, delay, packet loss, and cost efficiency. The test results clearly demonstrate that the PLA outperforms the conventional method, not only with the parameters mentioned above but also in terms of cost-effectiveness using master and slave controllers.