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.

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.

This paper aims to review the concept of Emotional Intelligence systematically. In essence, Emotional Intelligence pertains to our capacity to identify, comprehend, and regulate our own emotions, as well as the emotions of others, thereby fostering the cultivation of positive interpersonal connections. This study will examine the dimensions of Emotional Intelligence outlined by researchers, exploring its definitions, associations with age and gender, assessment methods, and overall advantages. Consequently, a comprehensive and methodical review of the existing literature was conducted using prominent academic databases such as Science Direct, Google Scholar, ResearchGate, and APA PsycNet. In summary, Emotional Intelligence is not a singular, homogeneous concept but a combination of various interconnected components. Integrating self-awareness, self-regulation, motivation, and empathy enables individuals to navigate their social environment effectively, rendering emotional intelligence (EI) a precious asset in both personal and professional domains.

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.

Background/Objectives: The following systematic review integrates neuroimaging techniques with deep learning approaches concerning emotion detection. It, therefore, aims to merge cognitive neuroscience insights with advanced algorithmic methods in pursuit of an enhanced understanding and applications of emotion recognition. Methods: The study was conducted following PRISMA guidelines, involving a rigorous selection process that resulted in the inclusion of 64 empirical studies that explore neuroimaging modalities such as fMRI, EEG, and MEG, discussing their capabilities and limitations in emotion recognition. It further evaluates deep learning architectures, including neural networks, CNNs, and GANs, in terms of their roles in classifying emotions from various domains: human-computer interaction, mental health, marketing, and more. Ethical and practical challenges in implementing these systems are also analyzed. Results: The review identifies fMRI as a powerful but resource-intensive modality, while EEG and MEG are more accessible with high temporal resolution but limited by spatial accuracy. Deep learning models, especially CNNs and GANs, have performed well in classifying emotions, though they do not always require large and diverse datasets. Combining neuroimaging data with behavioral and cognitive features improves classification performance. However, ethical challenges, such as data privacy and bias, remain significant concerns. Conclusions: The study has emphasized the efficiencies of neuroimaging and deep learning in emotion detection, while various ethical and technical challenges were also highlighted. Future research should integrate behavioral and cognitive neuroscience advances, establish ethical guidelines, and explore innovative methods to enhance system reliability and applicability.

The research aims to study the effects of economic policy uncertainty on the return volatility of stocks with data from the largest banking institutions in Greece. Volatility is constructed using intraday data, whereas the research period extends over a period of about thirteen years, more specifically from January 5, 2001, to June 30, 2014. This period includes various phases of the market, such as stock market crashes along with stock market booms (e.g. the financial crisis of 2007 and 2008 in the United States and the European sovereign debt crisis). The estimated regressions were used to indicate the direct effects of economic policy uncertainty on the return volatility of the stocks of the four large Greek banks. The volatility index is constructed based on intraday data, whereas four different estimators of volatility were used. There is a statistically significant "direct" effect of economic policy uncertainty on the volatility of stock returns of the largest Greek banks, which are (a) Alpha Bank, (b) Eurobank, (c) National Bank of Greece, and (d) Piraeus Bank. Such findings are highly important for specific groups of people, such as investors, policymakers, and regulators. This study is the first research that seeks to identify the effect of economic policy uncertainty on the stock return volatility of the Greek banking system, constructed from intraday data. Doi: 10.28991/ESJ-2022-06-03-011 Full Text: PDF