Explore the vast range of titles available.

The development of new technologies enables learners to learn more effectively, efficiently, flexibly and comfortably. Learners utilize smart devices to access digital resources through wireless network and to immerse in both personalized and seamless learning. Smart education, a concept that describes learning in digital age, has gained increased attention. This paper discusses the definition of smart education and presents a conceptual framework. A four-tier framework of smart pedagogies and ten key features of smart learning environments are proposed for foster smart learners who need master knowledge and skills of the 21 st century learning. The smart pedagogy framework includes class-based differentiated instruction, group-based collaborative learning, individual-based personalized learning and mass-based generative learning. Furthermore, a technological architecture of smart education, which emphasizes the role of smart computing, is proposed. The tri-tier architecture and key functions are all presented. Finally, challenges of smart education are discussed.

This study examines the research landscape of smart learning environments by conducting a comprehensive bibliometric analysis of the field over the years. The study focused on the research trends, scholar’s productivity, and thematic focus of scientific publications in the field of smart learning environments. A total of 1081 data consisting of peer-reviewed articles were retrieved from the Scopus database. A bibliometric approach was applied to analyse the data for a comprehensive overview of the trend, thematic focus, and scientific production in the field of smart learning environments. The result from this bibliometric analysis indicates that the first paper on smart learning environments was published in 2002; implying the beginning of the field. Among other sources, “Computers & Education,” “Smart Learning Environments,” and “Computers in Human Behaviour” are the most relevant outlets publishing articles associated with smart learning environments. The work of Kinshuk et al., published in 2016, stands out as the most cited work among the analysed documents. The United States has the highest number of scientific productions and remained the most relevant country in the smart learning environment field. Besides, the results also showed names of prolific scholars and most relevant institutions in the field. Keywords such as “learning analytics,” “adaptive learning,” “personalized learning,” “blockchain,” and “deep learning” remain the trending keywords. Furthermore, thematic analysis shows that “digital storytelling” and its associated components such as “virtual reality,” “critical thinking,” and “serious games” are the emerging themes of the smart learning environments but need to be further developed to establish more ties with “smart learning”. The study provides useful contribution to the field by clearly presenting a comprehensive overview and research hotspots, thematic focus, and future direction of the field. These findings can guide scholars, especially the young ones in field of smart learning environments in defining their research focus and what aspect of smart leaning can be explored.

This paper discusses the key characteristics of smart learning and the main challenges to be overcome when designing smart educational environments to support personalisation. In order to integrate smart learning environments into the learning ecosystem and educational contexts, innovative uses and new pedagogical approaches need to be implemented to orchestrate formal and informal learning. This contribution describes the main characteristics of smart learning and smart learning environments and sustains the relevance of taking the participation of future users into account during the design process to increase knowledge of the design and the implementation of new pedagogical approaches in smart learning environments.

Smart devices and intelligent technologies are enabling a smart learning environment to effectively promote the development of personalized learning and adaptive learning, in line with the trend of accelerating the integration of both. In this regard, we introduce a new teaching method enabled by a smart learning environment, which is a form of personalized adaptive learning. In order to clearly explain this approach, we have deeply analyzed its two pillars: personalized learning and adaptive learning. The core elements of personalized adaptive learning and its core concept are explored as well. The elements are four: individual characteristics, individual performance, personal development, and adaptive adjustment. And the core concept is referred to a technology-empowered effective pedagogy which can adaptively adjust teaching strategies timely based on real-time monitoring (enabled by smart technology) learners’ differences and changes in individual characteristics, individual performance, and personal development. On this basis, A framework of personalized adaptive learning is also constructed. Besides, we further explored a recommendation model of the personalized learning path. To be specific, personalized adaptive learning could be constructed from the following four aspects, namely, learner profiles, competency-based progression, personal learning, and flexible learning environments. Last, we explored a form of learning profiles model and a generative paths recommendation pattern of personal learning. This paper provides a clear understanding of personalized adaptive learning and serves as an endeavor to contribute to future studies and practices.

Personalized learning is a learning approach that aims to personalize the learning experience according to the unique needs, goals, and skills of individuals which can be achieved by using current instructional technology that provides unique learning experiences in different learning environments. Technology is the main component that will enable and enrich personalized learning experience; however, even though technology is available to personalize the learning experience, there is still a lack of unified agreement on what components need to be considered for a dynamic personalized learning approach that is to be able to provide a unique and effective learning experience to each learner. To address this need, this study aims to analyze and synthesize different personalized learning approaches that consider different learning components, so that we have an evolving agreement on personalized learning models and approaches. The findings of this research identified the following main components: learner profiles and attitudes, previous knowledge and beliefs, personalized adaptive learning paths, and flexible self-paced learning environments that are generated by learning analytics. These prominent characteristics imply that a personalized learning environment (PLE) would need to be dynamic to maintain a current record of learner interests and attitudes, past experiences and performance, and activities and interactions likely to match a particular learner and learning goal.

This study was conducted to investigate the effect of task and group awareness (TaGA) support provided to group members by a pedagogical agent (PA) in computer-supported collaborative learning (CSCL) on the students’ attitudes towards collaborative learning and self-regulated learning skills (SRLS). A quasi-experimental research design with pretest and posttest control groups and mixed methods were used in this study. Participants were undergraduate students (n = 42) enrolled in the Computing II course in their first year. Of the 42 university student, 15 (35.7%) were male and 27 (64.3%) were female. The participants were randomly assigned to the experimental and control groups. The findings of the study demonstrated that TaGA support provided to the members of the experimental group through the PA in CSCL fostered students’ attitudes towards online collaborative learning but did not affect their SRLS. The findings obtained from the qualitative data were in good agreement with the quantitative data. This study contributes to the field by providing practical suggestions on how the learning process and outcomes in CSCL can be improved through PA-based support and scaffolding.

Smart environments integrated with digital technologies have emerged as a powerful tool to support personalized, interactive, and adaptive learning experiences. The central objective of developing smart environment is to foster learner agency enabling learners to develop the ability to take ownership and manage their own learning process. However, developing learner agency presents a significant challenge. It requires researchers, practitioners, and system developers to understand the roles of teachers and students, the instructional design, and the affordances of technologies to transfer learners from passive recipients to active participants in their learning process. To address the need, this special issue collected twenty-nine articles which underwent a rigorous peer review process. The acceptance rate for this special issue was less than fifteen percent and only four papers were accepted and included. The four papers discuss the application of instructional approaches and various digital tools such as artificial intelligence, virtual reality, and mobile technologies to design smart learning environments which foster and evaluate learner agency.

This study proposes an empathy-aware intelligent system for smart learning environments, integrating multimodal emotional cues such as facial expressions, heart rhythms, and digital behaviors through a deep convolutional neural network (CNN) architecture. The framework employs a dynamic attention mechanism to fuse heterogeneous features, enabling context-aware adaptation to learners’ emotional states. Validated via real-world classroom trials and public datasets including DAiSEE and Affective MOOC, the model achieves 85.3% accuracy in detecting subtle emotional fluctuations, outperforming conventional methods by 12-18% in scenario-specific adaptability. Educational experiments demonstrate significant improvements, with a 21% increase in learner engagement and 37% higher acceptance of personalized interventions. Compared to existing approaches such as single-modality support vector machine or static fusion models, our design introduces two innovations: dedicated CNN sub-networks for modality-specific feature extraction and self-attention-based dynamic fusion that prioritizes critical signals under varying learning contexts. These advancements bridge the gap between technical metrics and pedagogical relevance, transforming engagement analytics into actionable insights for responsive educational ecosystems.

Advances in technology in recent years have changed the learning behaviors of learners and reshaped teaching methods. This had resulted in several challenges faced by current educational systems, such as an increased focus on informal learning, a growing gap of prior knowledge among students in classrooms and a mismatch between individual career choices and the development of the work force. This paper looks at these challenges with a view towards revolutionizing current learning environments to smart learning environments and provides new suggestions for technological solutions. Furthermore, this paper argues for a transformation from the current learning environments to smart learning environments. This is to be achieved by reengineering the fundamental structure and operations of current educational systems to better integrate these new technologies with the required pedagogical shift. The future perspectives of smart learning environments are reviewed and shared, through examples of emerging innovations such as the flipped classroom, game based learning, gesture based learning, along with pedagogical shifts, such as life-long learning portfolio maintenance, team teaching, and separation of learning and competency assessment.

In the contemporary digital age, education is no longer limited to traditional educational environments. Many educational institutions shifted to depend on the smart learning process but expressed concern about this solution due to its various challenges in securing the learning process and learners’ data. By virtue of the most recent technologies like blockchain and artificial intelligence, which played a significant role in solving many challenges that faced the educational sector and overcoming issues like fake certificates, manipulation, tracking learners’ activities, and predicting learners’ academic performance. The study proposed a smart framework based on blockchain and deep learning to enhance smart learning processes and provide solutions for challenges in the field. The framework is intended to store the learner’s data on the blockchain through the interplanetary file system and reap the benefits of securing the learner’s data and ensuring its integrity, as well as ensuring the confidentiality and authentication of the users through the wallets that are created on the Ethereum private blockchain platform. Then apply the deep learning model to this secured data to predict the learner’s performance. The smart contract functions also play a role in enabling the university to issue learners’ certificates that are stored on the blockchain to be available and verifiable by all the nodes in the network. Based on the experimental results, deep neural networks were used to model the encrypted data that was stored on the blockchain and predict the learner’s performance and achieved a high degree of accuracy (91.29%) and low loss (about 0.18) in comparison to other studies that depended on the centralized nature of the data. As well, the university blockchain’s functionality was tested, and it successfully returned all the functional requirements and showed its legitimacy.