Explore the vast range of titles available.

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.

Educational technology plays an increasingly significant role in supporting Self-Regulated Learning (SRL), while the importance of Adaptive Learning Technology (ALT) grows due to its ability to provide personalized support for learners. Despite recognizing the potential of ALT to be influential in SRL, effectively addressing pedagogical concerns about using ALT to enhance students’ SRL remains an ongoing challenge. Consequently, learners can develop perceptions that ALT is not customized to their specific needs, resulting in critical or dismissive attitudes towards such systems. This study therefore explores the potential of combining Natural Language Processing (NLP) to enhance real-time contextual adaptive learning within an ALT to support learners’ SRL. In addressing this question, our approach consisted of two steps. Initially, we focused on developing an ALT that incorporates learners’ needs. Subsequently, we explored the potential of NLP to capture pertinent learner information essential for providing adaptive support in SRL. In order to ensure direct applicability to pedagogical practice, we engaged in a one-year co-design phase with a high school. Qualitative data was collected to evaluate the implementation of the ALT and to check complementary possibilities to enhance SRL by potentially adding NLP. Our findings indicate that the learning technology we developed has been well-received and implemented in practice. However, there is potential for further development, particularly in terms of providing adaptive support for students. It is evident that a meaningful integration of NLP and ALT holds substantial promise for future enhancements, enabling sustainable support for learners SRL.

The last decade has seen a surge of interest in adaptive learning algorithms for data stream classification, with applications ranging from predicting ozone level peaks, learning stock market indicators, to detecting computer security violations. In addition, a number of methods have been developed to detect concept drifts in these streams. Consider a scenario where we have a number of classifiers with diverse learning styles and different drift detectors. Intuitively, the current ‘best’ (classifier, detector) pair is application dependent and may change as a result of the stream evolution. Our research builds on this observation. We introduce the Tornado framework that implements a reservoir of diverse classifiers, together with a variety of drift detection algorithms. In our framework, all (classifier, detector) pairs proceed, in parallel, to construct models against the evolving data streams. At any point in time, we select the pair which currently yields the best performance. To this end, we introduce the CAR measure, which is employed to balance classification, adaptation and resource utilization requirements. We further incorporate two novel stacking-based drift detection methods, namely the FHDDMS and \\[ FHDDMS_add\\] approaches. The experimental evaluation confirms that the current ‘best’ (classifier, detector) pair is not only heavily dependent on the characteristics of the stream, but also that this selection evolves as the stream flows. Further, our FHDDMS variants detect concept drifts accurately in a timely fashion while outperforming the state-of-the-art.

This research presents the Cross-Cultural Intelligent Language Learning System (CILS), a novel approach integrating artificial intelligence (AI) into language education to enhance cross-cultural communication. CILS utilizes advanced AI technologies to provide adaptive, personalized learning experiences that cater to the unique linguistic and cultural backgrounds of each learner. By dynamically adjusting content and methodology, CILS significantly improves linguistic proficiency and cultural understanding, essential for effective global interactions. The implementation of CILS in platforms such as Busuu and HelloTalk has demonstrated marked improvements in engagement and communication skills among learners. Empirical studies validate the system’s effectiveness in real-world settings, showing enhanced learner performance and increased intercultural competence. Additionally, the Technology Acceptance Model (TAM) applied confirms that the usability and perceived usefulness of AI-driven systems strongly influence learner acceptance and sustained use. This study not only underscores the potential of AI in transforming language education but also highlights the critical role of cultural sensitivity in designing educational technologies.

Using adaptive learning technologies, personalized feedback, and interactive AI tools, this study investigates how these tools affect student engagement and what the mediating role of individuals’ digital literacy is at the same time. The study will target 500 students from different faculties such as science, engineering, humanities, and social sciences. With the changing trends in educational technology, it is important to know if these tools allow students to interact with learning materials. Through this study, we explore how adaptive learning technologies, which adapt content to students’ progress, are influenced by student motivation and participation during the learning process using AI tools that provide real-time feedback and interaction. Also, digital literacy is presented as a moderating factor that may either accelerate or impede the effectiveness of these tools. These findings demonstrate that more adaptive learning technologies, which have organized feedback, and interactive AI tools help improve student engagement. Additionally, students with higher levels of digital literacy are more involved with digital tools. This research recognizes that teachers should incorporate these technologies into their courses in such a manner as it synergizes with student’s digital capabilities to reap the benefits of technology on students’ engagement and learning outcomes.

Wearable sensor technology is increasingly being integrated into educational settings, offering innovative approaches to enhance teaching and learning experiences. These devices track various physiological and environmental variables, providing valuable insights into student engagement, comprehension, and educational environments. However, the extensive and continuous data streams generated by these sensors create significant challenges for learning analytics. This paper presents a comprehensive review of research on learning analytics incorporating wearable technology, systematically identifying methods and approaches that address wearable sensor data challenges. We begin with a systematic review of wearable sensor technologies’ historical development and the current state of sensor data in learning analytics. We then examine multimodal sensor applications in learning analytics and propose research and application trends aligned with educational development needs. Our analysis identifies three key challenges: ethical considerations, explainable learning analytics, and technological and data management issues. The paper concludes by outlining seven future development directions for wearable sensors in educational contexts.

Controlling complex nonlinear dynamical systems using traditional methods has always been a difficult task because the majority of systems seen in nature have intricate nonlinear mathematical relationships. Artificial neural network (ANN) models are a good option for handling such intricate nonlinear systems since they include a number of significant properties like faster learning, adaptation, parallel processing, and nonlinear mapping capabilities. Several recurrent neural networks (RNNs)-based controllers have been suggested in the literature for implementing adaptive control, but the majority of these models have extremely complex topologies and many of them are challenging to train. In this paper, an attempt is made to put forward the RNN model (called as higher-order recurrent neural network (HORNN)) which is based on a higher order Pi-Sigma neural network (PSNN) model and implemented for the indirect adaptive control of the nonlinear dynamical system. The parameters of the proposed controller are tuned using the gradient-descent-based asynchronous back-propagation (BP) method. The proposed controller consists of two additional internal feedback loop layers (denoted by F L 1 and F L 2 ) corresponding to the hidden and the output layer, respectively. The nodes present in F L 1 and F L 2 layers are having weighted connections with the hidden and the output layer neurons, respectively, and these feedback connections enrich the controller with a memory property. The second contribution of the paper is to improve the performance of the learning algorithm which is achieved by incorporating an adaptive learning rate scheme (that ensures the correct setting of the learning rate value in each iteration). Another advantage of the HORNN-based controller is that it is only provided with three inputs irrespective of the dynamics of the plant and only 3 hidden neurons are included in its hidden layer (this reduces the overall structural complexity of the proposed model). The performance of the HORNN-based controller is compared with some of the popular neural networks such as diagonal recurrent neural network (DRNN), Jordan recurrent neural network (JRNN), feed-forward neural network (FFNN), and PSNN. Through simulation experiments, it is observed that the response obtained from the plant under HORNN-based controller is found to be better as compared to responses obtained with other ANN-based controllers. Further, the instantaneous mean square error (IMSE) obtained with HORNN-based controller is quite less and is equal to 0.058 as compared to 0.077, 0.082, 1.74, 13.43, and 1.86 with DRNN, JRNN, PSNN, FFNN, and FFNN (with 30 hidden neurons)-based controllers, respectively.

The present survey examines the intersection of methodological advancements and technological innovations in e-learning, emphasizing their transformative impact on modern education. It systematically explores instructional design frameworks, adaptive learning systems, immersive technologies, and data-driven analytics, highlighting their role in fostering personalized, scalable, and inclusive learning environments. Through the integration of pedagogical theories with advanced tools like artificial intelligence (AI), augmented reality (AR), virtual reality (VR), and mixed reality (MR), this study demonstrates how e-learning systems enhance engagement, retention, and accessibility. The survey addresses critical challenges such as the digital divide, data privacy, and resistance to adoption, offering evidence-based strategies to mitigate these issues. It underscores the importance of bridging equity gaps while maintaining scalability and sustainability, particularly in underserved regions. By synthesizing state-of-the-art research and practical applications, this work provides actionable insights into the future of e-learning, advocating for a balanced approach to innovation that aligns technological capabilities with the diverse needs of global learners. The findings contribute to the broader discourse on sustainable, inclusive, and effective digital education ecosystems.

This paper presents a novel approach for personalized learning path generation by integrating deep knowledge tracing and cognitive load estimation within a unified framework. We propose a dual-stream neural network architecture that simultaneously models students’ knowledge states and cognitive load levels to optimize learning trajectories. The knowledge state tracking module employs a bidirectional Transformer with graph attention mechanisms to capture complex relationships between knowledge components, while the cognitive load estimation module utilizes multimodal data analysis to dynamically assess mental effort during learning activities. A dual-objective optimization algorithm balances knowledge acquisition with cognitive load management to generate paths that maintain optimal challenge levels. Experimental evaluations across multiple educational domains demonstrate that our approach outperforms existing methods in prediction accuracy (87.5%), path quality (4.4/5), and learning efficiency (24.6% improvement). The implemented system supports real-time adaptation based on performance and cognitive state, resulting in reduced frustration, higher engagement, and improved knowledge retention. This research contributes to both theoretical understanding of learning processes and practical implementation of next-generation adaptive educational technologies.

Medical information systems such as Internet of Medical Things (IoMT) are gained special attention over recent years. X-ray and MRI images are important sources of information to be examined for a particular type of anomalies. Reports based on the images and laboratory examination results could be mined with machine learning techniques as well. Thyroid disease diagnosis is an important capability of medical information systems. The main objective of this study is to improve the diagnosis accuracy of thyroid diseases from semantic reports and examination results using artificial neural network (ANN) in IoMT systems. In order to improve generalization and avoid over-fitting of ANN during the training process, a set of multiple multilayer perceptron (MMLP) neural network with the back-propagation error ability is proposed in this paper. Moreover, an adaptive learning rate algorithm is used to deal with the slow convergence and the local minima problem of the back-propagation error algorithm. The proposed MMLP significantly increased the overall accuracy of thyroid disease classification. With MMLP with a set of 6 networks, an improvement of 0.7% accuracy is achieved compared to a single network. In addition, comparing to the standard back-propagation, by using an adaptive learning rate algorithm in the proposed MMLP, an improvement of 4.6% accuracy and the final accuracy of 99% have been obtained in IoMT systems. The proposed MMLP is compared to recent researches reported for thyroid disease diagnosis, and its superiority is shown.