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.

Background/Objectives: This systematic review integrates Cognitive Load Theory (CLT), Educational Neuroscience (EdNeuro), Artificial Intelligence (AI), and Machine Learning (ML) to examine their combined impact on optimizing learning environments. It explores how AI-driven adaptive learning systems, informed by neurophysiological insights, enhance personalized education for K-12 students and adult learners. This study emphasizes the role of Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS), and other neurophysiological tools in assessing cognitive states and guiding AI-powered interventions to refine instructional strategies dynamically. Methods: This study reviews n = 103 papers related to the integration of principles of CLT with AI and ML in educational settings. It evaluates the progress made in neuroadaptive learning technologies, especially the real-time management of cognitive load, personalized feedback systems, and the multimodal applications of AI. Besides that, this research examines key hurdles such as data privacy, ethical concerns, algorithmic bias, and scalability issues while pinpointing best practices for robust and effective implementation. Results: The results show that AI and ML significantly improve Learning Efficacy due to managing cognitive load automatically, providing personalized instruction, and adapting learning pathways dynamically based on real-time neurophysiological data. Deep Learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Support Vector Machines (SVMs) improve classification accuracy, making AI-powered adaptive learning systems more efficient and scalable. Multimodal approaches enhance system robustness by mitigating signal variability and noise-related limitations by combining EEG with fMRI, Electrocardiography (ECG), and Galvanic Skin Response (GSR). Despite these advances, practical implementation challenges remain, including ethical considerations, data security risks, and accessibility disparities across learner demographics. Conclusions: AI and ML are epitomes of redefinition potentials that solid ethical frameworks, inclusive design, and scalable methodologies must inform. Future studies will be necessary for refining pre-processing techniques, expanding the variety of datasets, and advancing multimodal neuroadaptive learning for developing high-accuracy, affordable, and ethically responsible AI-driven educational systems. The future of AI-enhanced education should be inclusive, equitable, and effective across various learning populations that would surmount technological limitations and ethical dilemmas.

Objectives: Recently, due to special conditions that the global community is experiencing (Covid-19), there is growing interest in research in educational leadership, especially in higher education, which will create an environment embedded in a collaborative culture, open to improvements, testing, and often unexpected challenges. Among the different forms of educational leadership, transformational leadership is recognized as the most appropriate for application in higher education, as it focuses on the division of leadership among academics with different skills in order to manage in a collective way the range of leadership duties required in different contexts. Methods/Analysis: This view is also reflected in this research paper, which focused on the degree of three specific forms of leadership (transformational leadership, transactional leadership, and leadership to be avoided) by members of the Senate of the University of Peloponnese. Descriptive and inductive methods of statistical analyses were applied for the interpretation of results. Findings: Notably, the findings indicate that leadership outcome has a strong positive correlation with transformational leadership and negative correlation with passive-to avoid leadership, confirming that higher transformational leadership implies greater efficiency and satisfaction for employees. Novelty/Improvement:A high degree of passive leadership coexists with the corresponding negative degree of implementation of digital leadership. Doi: 10.28991/esj-2021-01252 Full Text: PDF

Background/Objectives: This systematic review presents how neural and emotional networks are integrated into EEG-based emotion recognition, bridging the gap between cognitive neuroscience and practical applications. Methods: Following PRISMA, 64 studies were reviewed that outlined the latest feature extraction and classification developments using deep learning models such as CNNs and RNNs. Results: Indeed, the findings showed that the multimodal approaches were practical, especially the combinations involving EEG with physiological signals, thus improving the accuracy of classification, even surpassing 90% in some studies. Key signal processing techniques used during this process include spectral features, connectivity analysis, and frontal asymmetry detection, which helped enhance the performance of recognition. Despite these advances, challenges remain more significant in real-time EEG processing, where a trade-off between accuracy and computational efficiency limits practical implementation. High computational cost is prohibitive to the use of deep learning models in real-world applications, therefore indicating a need for the development and application of optimization techniques. Aside from this, the significant obstacles are inconsistency in labeling emotions, variation in experimental protocols, and the use of non-standardized datasets regarding the generalizability of EEG-based emotion recognition systems. Discussion: These challenges include developing adaptive, real-time processing algorithms, integrating EEG with other inputs like facial expressions and physiological sensors, and a need for standardized protocols for emotion elicitation and classification. Further, related ethical issues with respect to privacy, data security, and machine learning model biases need to be much more proclaimed to responsibly apply research on emotions to areas such as healthcare, human–computer interaction, and marketing. Conclusions: This review provides critical insight into and suggestions for further development in the field of EEG-based emotion recognition toward more robust, scalable, and ethical applications by consolidating current methodologies and identifying their key limitations.

Objectives: Notably, as a result of the unique circumstances that the global population is facing (Covid-19), there has been an increase in interest in educational leadership science, particularly in academic environment, with the aim of enhancing an ecosystem that is inclusive, open to change, monitoring, and often unforeseen challenges. Of the various types of educational leadership, transformational leadership is recognized as the most suitable for use in academic environment. It focuses on the division of leadership responsibilities among academics with varying skills in order to handle collectively the spectrum of leadership responsibilities needed in a variety of contexts. Methods/Analysis: This perspective is embodied in this study paper, which examined the degree to which members of the Senate of three Universities of Greece, engaged in three distinct types of leadership (transformational leadership, transactional leadership, and leadership to be avoided) via digital leadership. The findings were interpreted using descriptive, diagnostic, predictive and prescriptive data analysis algorithms. Findings: The findings indicate that participants demonstrate a high level of Transformational and Transactional Leadership. They demonstrated a high level of Digital Leadership while avoiding Passive - To Avoid Leadership. The study demonstrates that Digital Leadership has a relatively favorable association with the leadership outcome, which is translated as a high level of performance and satisfaction coexisting with a high level of digital leadership execution. Novelty /Improvement: The research project indicates that a good leader who practices transformational leadership and is prepared with the necessary digital skills would be more effective in carrying out administrative responsibilities in an academic environment. Doi: 10.28991/esj-2021-01286 Full Text: PDF

Background: This systematic review will address the influence of Information and Communication Technologies (ICTs) on democratic processes and citizens’ participation, which is enabled by such tools as social media, e-voting systems, e-government initiatives, and e-participation platforms. Methods: Based on an in-depth analysis of 46 peer-reviewed articles published between 1999 and 2024, this review emphasizes how ICTs have improved democratic engagement quality, efficiency, and transparency, but highlights key challenges and research gaps. Results: From this angle, ICT tools have great potential to nurture civic engagement and good governance through transparency. Challenges persist with the ethical and social implications of surveillance technologies, security concerns about digital voting systems, and the widening digital divide disproportionately affecting marginalized populations. The current regulatory framework dealing with privacy and misinformation issues is relatively weak, and there is also a lack of understanding about ICTs’ long-term effects on democratic governance. Conclusions: This review underlines the duality of the roles played by ICT as both an enabler and a challenge to democratic processes. It calls for regulatory measures to protect privacy, fight disinformation, and reduce the digital divide. Future research in this area should focus on the long-term effects of ICTs and how they can be equitably and efficiently integrated into democratic systems, with strategies aimed at maximizing benefits while minimizing risks.