This overview examines recent advancements in EEG-based biometric identification, focusing on integrating emotional recognition to enhance the robustness and accuracy of biometric systems. By leveraging the unique physiological properties of EEG signals, biometric systems can identify individuals based on neural responses. The overview discusses the influence of emotional states on EEG signals and the consequent impact on biometric reliability. It also evaluates recent emotion recognition techniques, including machine learning methods such as support vector machines (SVMs), convolutional neural networks (CNNs), and long short-term memory networks (LSTMs). Additionally, the role of multimodal EEG datasets in enhancing emotion recognition accuracy is explored. Findings from key studies are synthesized to highlight the potential of EEG for secure, adaptive biometric systems that account for emotional variability. This overview emphasizes the need for future research on resilient biometric identification that integrates emotional context, aiming to establish EEG as a viable component of advanced biometric technologies.

This overview examines recent advancements in EEG-based biometric identification, focusing on integrating emotional recognition to enhance the robustness and accuracy of biometric systems. By leveraging the unique physiological properties of EEG signals, biometric systems can identify individuals based on neural responses. The overview discusses the influence of emotional states on EEG signals and the consequent impact on biometric reliability. It also evaluates recent emotion recognition techniques, including machine learning methods such as Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Long Short-Term Memory networks (LSTM). Additionally, the role of multimodal EEG datasets in enhancing emotion recognition accuracy is explored. Findings from key studies are synthesized to highlight the potential of EEG for secure, adaptive biometric systems that account for emotional variability. This overview emphasizes the need for future research on resilient biometric identification that integrates emotional context, aiming to establish EEG as a viable component of advanced biometric technologies.

Well-being and happiness are increasingly recognized as key outcomes in higher education, yet few Latin American public universities systematically assess them at the institutional level. This study reports the results of an institutional measurement of well-being and happiness, based on the SPIRE model (Spiritual, Physical, Intellectual, Relational, and Emotional) and NPS, applied to the entire academic community. The database was collected through a digital survey grounded in the SPIRE dimensions, administered to students, faculty, staff, and contractors at a Colombian public university. A total of 877 complete responses were obtained from a population of 8362 individuals. Data were analyzed using descriptive and inferential statistics, psychometric analysis, clustering, and supervised classification techniques. The findings reveal differences in well-being across institutional roles, with faculty and staff reporting higher levels of overall well-being than students, particularly in the physical and emotional dimensions. The analysis also highlights patterns in perceived life purpose, interpersonal relationships, and intellectual well-being. The Random Forest classifier achieved an accuracy of 95.8% in reproducing membership in data-driven well-being clusters derived from the SPIRE dimensions. This classification performance reflects the reproducibility of the cluster structure within the same institutional dataset and should not be interpreted as evidence of future well-being prediction or independent predictive validity.

Generative artificial intelligence (GenAI) is increasingly being integrated into educational practices, transforming how students access information, develop ideas, and carry out cognitive tasks. While GenAI offers opportunities to foster critical thinking and creativity, its use raises concerns regarding "cognitive offloading"—the delegation of reasoning processes that are fundamental to learning. This study presents a review based on 108 documents from Scopus and Web of Science, which allowed for an examination of the relationships between GenAI, critical thinking, creativity, and cognitive offloading, as well as their pedagogical and institutional implications. The evidence analyzed suggests that GenAI can improve immediate task performance, facilitate idea generation, and support content development. However, these benefits do not necessarily translate into autonomous learning or greater creative originality. Outcomes appear to depend on students' prior knowledge, their AI literacy, task design, metacognitive regulation, and the nature and timing of pedagogical support. Based on this review, an AI-Mediated Cognitive Infrastructure Framework (AMCIF) is proposed, which links GenAI usage patterns with cognitive processes, educational practices, and institutional conditions. This framework distinguishes between forms of assistance that promote cognitive engagement and those that might replace the mental activity necessary for learning, emphasizing the essential role of instructor mediation, source verification, reflective practices, and process-oriented assessment. The AMCIF provides a conceptual foundation for analyzing how to integrate GenAI into educational settings while preserving critical judgment, creativity, and the capacity for independent reasoning. The proposed relationships require rigorous empirical validation.

Electroencephalographic (EEG) signals have been extensively studied for emotion detection and, more recently, as an alternative for biometric identification and authentication. Biometric methods based on physiological signals are a non-conventional approach for personal identification, and their study is currently considered an open research field. However, EEG-based biometric systems face several challenges, including the influence of emotional states, which can affect their performance. This study evaluates the effect of emotional states on the performance of an EEG-based biometric system. Four widely used databases for biometrics and emotion recognition (DEAP, MAHNOB, SEED, and LUMED-2) were selected for analysis. Feature extraction was performed using multiple strategies in the time, frequency, and time–frequency domains. The performance of various classifiers—support vector machine (SVM), random forest (RF), artificial neural networks (ANN), and k-nearest neighbors (K-NN)—was evaluated separately. Furthermore, stacking was used as a classifier fusion method. Explicit modeling of emotional states contributed to improving classifier performance. The best model based on classifier fusion achieved an accuracy of 95.73 ± 1.83%. These results indicate that incorporating information about emotional state into EEG-based biometric systems can contribute to the development of more robust and realistic identification solutions.