Search Results (1,270)

Whether it is asking Alexa to set a reminder or having Google Assistant place a call, AI-powered assistants are becoming an increasingly seamless part of our daily lives. This study aims to address what predicts the users’ satisfaction with Alexa by analyzing the using motives, cognitive involvement, and emotional involvement of its consumers. The variables include using motives, attention, elaboration, emotional involvement, and usage satisfaction. Alexa users (N = 299) completed a brief online survey, including Scales of Using Motives for Media, the Perceived Attention Scale, the Elaboration Scale, the Mood Adjective Check List Scale, and Television Viewing Satisfaction Scale. Participants who were at least eighteen years of age and owned and used Alexa were included in this study. An exploratory factor analysis revealed four distinct types of motivation for using Alexa: Companionship, Entertainment–Information, Work-Efficiency, and Pastime. The results from hierarchical regressions showed that Alexa usage satisfaction was predicted by Entertainment–Information and feeling positive emotions while using. Full article

Introduction: Previous neuroimaging studies have reported structural brain alterations and local network hyperexcitability in terms of increased slow-wave electroencephalography (EEG) activity in patients with borderline personality disorder (BPD). In particular, intermittent rhythmic delta and theta activity (IRDA/IRTA) has drawn attention in mental health contexts due to its links with metabolic imbalances, neuronal stress, and emotional dysregulation—processes that are highly pertinent to BPD. These functional disturbances may be reflected in corresponding structural brain changes. The current study investigated cortical thickness and subcortical volumes in BPD and examined their associations with IRDA/IRTA events per minute, symptom severity, and neuropsychological measures. Methods: Seventy female BPD patients and 36 age-matched female healthy controls (HC) were included (for clinical EEG comparisons even 72 patients were available). IRDA/IRTA rates were assessed using an automatic independent component analyses (ICA) approach. T1-weighted MRI data were obtained using a MAGNETOM Prisma 3T system and analyzed with FreeSurfer (version 7.2) for subcortical structures and CAT12 for cortical thickness and global volume measurements. Psychometric assessments included questionnaires such as Borderline Symptom List (BSL-23) and Inventory of Personality Organization (IPO). Neuropsychological performance was evaluated with the Test for Attentional Performance (TAP), Culture Fair Intelligence Test (CFT-20-R), and Verbal Learning and Memory Test (VLMT). Results: Between-group comparisons exhibited no significant increase in IRDA/IRTA rates or structural abnormalities between the BPD and HC group. However, within the BPD group, cortical thickness of the right isthmus of the cingulate gyrus negatively correlated with the IRDA/IRTA difference (after minus before hyperventilation, HV; p < 0.001). Furthermore, BPD symptom severity (BSL-23) and IPO scores positively correlated with the thickness of the right rostral anterior cingulate cortex (p < 0.001), and IPO scores were associated with the thickness of the right temporal pole (p < 0.001). Intrinsic alertness (TAP) significantly correlated with relative cerebellar volume (p = 0.01). Discussion: While no group-level structural abnormalities were observed, correlations between EEG slowing, BPD symptom severity, and alertness with cortical thickness and/or subcortical volumes suggest a potential role of the anterior cingulate cortex, temporal pole, and cerebellum in emotion regulation and cognitive functioning in BPD. Future research employing multimodal EEG-MRI approaches may provide deeper insights into the neural mechanisms underlying BPD and guide personalized therapeutic strategies. Full article

This paper presents an intelligent navigation wearable device for visually impaired individuals. The system aims to improve their independent travel capabilities and reduce the negative emotional impacts associated with visible disability indicators in travel tools. It employs an RGB-D camera and an inertial measurement unit (IMU) sensor to facilitate real-time obstacle detection and recognition via advanced point cloud processing and YOLO-based target recognition techniques. An integrated intelligent interaction module identifies the core obstacle from the detected obstacles and translates this information into multidimensional auxiliary guidance. Users receive haptic feedback to navigate obstacles, indicating directional turns and distances, while auditory prompts convey the identity and distance of obstacles, enhancing spatial awareness. The intuitive vibrational guidance significantly enhances safety during obstacle avoidance, and the voice instructions promote a better understanding of the surrounding environment. The device adopts an arm-mounted design, departing from the traditional cane structure that reinforces disability labeling and social stigma. This lightweight mechanical design prioritizes user comfort and mobility, making it more user-friendly than traditional stick-type aids. Experimental results demonstrate that this system outperforms traditional white canes and ultrasonic devices in reducing collision rates, particularly for mid-air obstacles, thereby significantly improving safety in dynamic environments. Furthermore, the system’s ability to vocalize obstacle identities and distances in advance enhances spatial perception and interaction with the environment. By eliminating the cane structure, this innovative wearable design effectively minimizes social stigma, empowering visually impaired individuals to travel independently with increased confidence, ultimately contributing to an improved quality of life. Full article

Integrating technology-enhanced gamification into teacher education can significantly foster motivation and reshape perceptions toward science learning. This mixed-methods case study explores how a 14-week course for preservice early childhood teachers, supported by the digital gamification platform FantasyClass and enriched with a cohesive narrative structure, impacted students’ motivation and attitudes towards science. The course featured structural gamification elements—such as experience points, digital collectibles, and team-based challenges—combined with immersive storytelling that contextualized scientific tasks within a fantasy adventure. Quantitative data from pre- and post-intervention surveys revealed statistically significant improvements in attitudes toward science and perceived teaching competence. Thematic analysis of qualitative feedback highlighted enhanced engagement, enjoyment, and relevance of science learning. These findings suggest that intelligent integration of gamified technologies and narrative design in science teacher initial training can address motivational barriers and foster positive emotional engagement. While context-specific, this study offers insights into how digital gamified learning environments can support the development of positive attitudes towards science among future early childhood educators. Full article

Background: Cognitive functions and brain connectivity could be influenced by psychoanalytic psychotherapy (PP), thus representing neurobiological parameters for therapy-induced changes. This study searched empirical studies on cognition and the brain to evaluate which functions have been assessed, with which instruments, and what changes have been documented in brain connectivity after PP. Methods: We used the guidelines and checklist of the Preferred Reporting Items of Systematic Reviews and Meta-analyses Extension for Scoping Reviews. The literature search was performed on the Medline–PubMed, American Psychological Association-PsycINFO, Elton Bryson Stephens Company, and Cochrane databases, and Google Scholar, including articles on patients with non-psychotic disturbances published from 1980 to September 2024. Results: Fifty-nine articles were collected. Five articles reported on cognitive outcomes. Abstraction and mentalization remained stable after individual PP in patients with adjustment disorders or anorexia nervosa. Executive functions, emotional intelligence, spatial short-term memory, attention, and balance between relatedness and self-definition improved after group PP applied alone or combined with individual PP. Twelve studies using functional magnetic resonance imaging, positron emission tomography, single-photon emission computerized tomography, or electroencephalography showed functional brain changes after different types of PP. Conclusions: An empirical approach has rarely been used to evaluate the impact of PP on the brain and cognition. The results of selected studies on neurotic and depressive disorders suggest that PP can stimulate cognitive function and brain connectivity. Further literature reviews are needed to clarify these issues and provide an avenue for research studies targeting PP in different conditions. Communication between neurology and psychoanalysis is indispensable. Full article

This study investigates the integration of mentorship, self-regulated learning (SRL), and generative artificial intelligence (gen-AI) to support pre-service teachers (PSTs) before and during work-integrated learning (WIL) placements. Utilising the Mentoring and SRL Pyramid Model (MSPM), it examines how mentors’ dual roles as coaches and assessors influence PSTs’ SRL and explores to what extent gen-AI can assist PSTs in meeting the demands of WIL placements. Quantitative and qualitative data from 151 PSTs, including survey, interview, placement scores, and mentor feedback were analysed using statistical correlation analysis and thematic analysis to reveal varied mentorship approaches. Gen-AI tools are highlighted as valuable in enhancing PSTs’ SRL, providing tactical and emotional guidance where traditional mentorship is limited. However, challenges remain in gen-AI’s ability to navigate complex interpersonal dynamics. The study advocates for balanced mentorship training that integrates technical and emotional support, and equitable access to gen-AI tools. These insights are critical for educational institutions aiming to optimise PST experiences and outcomes in WIL through strategic integration of gen-AI and mentorship. Full article

The Fourth Industrial Revolution (4IR), characterized by the integration of advanced digital technologies, is transforming industries globally and significantly impacting leadership practices, particularly in the healthcare sector. As organizations adapt to this digital evolution, the demand for effective leadership becomes increasingly critical. This paper explores Leadership 4.0, a framework that identifies the skills and competencies required for successful leadership in the digital age. The study employs a mixed-methods approach, combining a Systematic Literature Review (SLR) with qualitative insights gathered through case studies and the Delphi method, involving healthcare leaders, to investigate leadership practices in organizations undergoing digital transformation. Through case studies and literature reviews, the research seeks to provide a comprehensive understanding of the changing leadership landscape, addressing the unique challenges and opportunities presented by digital transformation in healthcare. The findings indicate that effective Leadership 4.0 requires a combination of digital literacy, strategic foresight, and emotional intelligence. Leaders must exhibit adaptability, drive innovation, and foster a culture of continuous learning to navigate the complexities of digital transformation successfully. The study also emphasizes the importance of internal branding as a critical strategy for aligning employees with the organization’s mission and digital vision, thereby fostering collective commitment to transformation objectives. Key challenges include resistance to change and the ongoing need for upskilling, while opportunities lie in leveraging digital innovations to enhance organizational performance. Full article

This study explores the relationship between addictive behaviors, online behaviors, and adolescents’ ability to manage conflict. Effective conflict management is influenced by emotional self-regulation, empathy, and the digital environment. The literature suggests that deficits in these areas may exacerbate antisocial behavior and social adjustment difficulties. This study evaluates the influence of emotional intelligence, addictive behaviors, and online behaviors on conflict management. The hypotheses include adolescents with a high level of emotional adjustment who manage conflicts more effectively (H2) and that addictive and problematic online behaviors increase the risk of conflict escalation (H1, H3). The research involved 403 adolescents, selected through non-probabilistic methods using structured questionnaires. Statistical analysis included normality tests, Spearman’s correlations, and Mann-Whitney U tests to identify group differences. Adolescents with high emotional adjustment obtained significantly higher scores in conflict management and emotional self-regulation. Addictive and problematic online behaviors are associated with more significant difficulties in conflict management. Adolescents involved in minor crimes demonstrate deficits in emotion and conflict management. The results confirm the essential role of emotional intelligence in conflict management and the negative impact of addictive and problematic online behavior. Educational interventions promoting emotional intelligence development and responsible use of technology can reduce antisocial behaviors and improve adolescents’ social adjustment. Full article

Employees have both work and non-work lives, and these domains of investment can interfere with each other. The present investigation (total N = 497) sought to understand the potential role(s) of work-related emotional intelligence (W-EI) in managing these forms of conflict, with samples consisting of full-time military personnel (Study 1), postdoctoral researchers (Study 2), and employees from diverse occupations (Study 3). Higher levels of W-EI were associated with lower levels of family-to-work conflict, but not work-to-family conflict, suggesting an asymmetric form of conflict shielding. Lesser experiences of family-work conflict also provided some explanation for why employees with higher W-EI levels were less prone to counterproductive work behaviors and work-related burnout. In addition, employees with higher W-EI levels were less prone to counterproductive work behaviors even when levels of family-work conflict were relatively high. The results highlight multiple ways in which employees with high W-EI levels manage the family-work interface. Full article

The integration of generative artificial intelligence (GAI) into higher education is transforming students’ learning processes, academic performance, and psychological well-being. Despite the increasing adoption of GAI tools, the mechanisms through which students’ AI literacy and self-regulated learning (SRL) relate to their academic and emotional experiences remain underexplored. This study investigates how AI literacy and SRL are associated with writing performance and digital well-being among university students in GAI-supported higher learning contexts. A survey was administered to 257 students from universities in China, and structural equation modeling was used to examine the hypothesized relationships. Results show that both AI literacy and SRL significantly and positively predict students’ writing performance, with SRL having a stronger effect. Moreover, AI literacy shows a positive association with GAI-driven well-being, with writing performance serving as a partial mediator in this relationship. These findings suggest that fostering both technological competencies and effective learning strategies may support students’ academic outcomes while supporting their psychological well-being in AI-enriched educational environments. By integrating AI literacy and SRL into a unified model, this study contributes to the growing body of research on GAI-driven well-being in higher education and offers practical implications for cultivating balanced and sustainable learning experiences in the age of GAI. Full article

Access control systems protect homes and businesses in the continually evolving security industry. This paper designs and implements a Raspberry Pi-based facial recognition door lock system using artificial intelligence and computer vision for reliability, efficiency, and usability. With the Raspberry Pi as its CPU, the system uses facial recognition for authentication. A camera module for real-time image capturing, a relay module for solenoid lock control, and OpenCV for image processing are essential. The system uses the DeepFace library to detect user emotions and adaptive learning to improve recognition accuracy for approved users. The device also adapts to poor lighting and distances, and it sends real-time remote monitoring messages. Some of the most important things that have been achieved include adaptive facial recognition, ensuring that the system changes as it is used, and integrating real-time notifications and emotion detection without any problems. Face recognition worked well in many settings. Modular architecture facilitated hardware–software integration and scalability for various applications. In conclusion, this study created an intelligent facial recognition door lock system using Raspberry Pi hardware and open-source software libraries. The system addresses traditional access control limits and is practical, scalable, and inexpensive, demonstrating biometric technology’s potential in modern security systems. Full article

The Industry 4.0 era has brought significant changes in all areas of everyday life. This development has an impact on employment relations, making evaluation a core aspect of human resource management, especially in education, where new skills and knowledge are important. Thus, the role of educational leaders as managers and evaluators of the educational staff, and especially, the characteristics/skills they should possess for an effective employee evaluation, are essential for a positive work environment. This study explores the qualitative characteristics that the educational unit manager should have to implement formative assessment practices effectively, and specifically, the mediating role of resilience and self-efficacy in the relationship between emotional intelligence and formative assessment. The sample consisted of 258 educational unit managers serving in primary public schools in the prefectures of Pieria, Imathia, Pella, Kilkis, and Chalkidiki, Greece. A self-report questionnaire was administered online using four scales: TEIQue-SF for emotional intelligence, the Multidimensional Teachers’ Resilience Scale, the New Generalized Self-Efficacy Scale, and the Teacher Formative Assessment Practice Scale. The model was evaluated with SPSS.20 and PLS-SEM v. 4.1.0.6. The results indicate that resilience plays the most crucial role in the model, acting as a key mediator between emotional intelligence and formative assessment. While self-efficacy also has a mediating role, it is effective only when combined with resilience, rather than functioning independently. This research’s results suggest a necessity for implementing professional development programs within schools for both educational managers and teachers. These findings have practical implications for in-service training of educational leaders in emotional intelligence and resilience-based HR practices. Full article

In the research on intelligent perception, dynamic emotion recognition has been the focus in recent years. Small samples and unbalanced data are the main reasons for the low recognition accuracy of current technologies. Inspired by circular convolution networks, this paper innovatively proposes an adaptive dynamic micro-expression recognition algorithm based on self-supervised learning, namely MADV-Net. Firstly, a basic model is pre-trained with accurate tag data, and then an efficient facial motion encoder is used to embed facial coding unit tags. Finally, a cascaded pyramid structure is constructed by the multi-level adaptive dynamic encoder, and the multi-level head perceptron is used as the input into the classification loss function to calculate facial micro-motion features in the dynamic video stream. In this study, a large number of experiments were carried out on the open-source datasets SMIC, CASME-II, CAS(ME)^2, and SAMM. Compared with the 13 mainstream SOTA methods, the average recognition accuracy of MADV-Net is 72.87%, 89.94%, 83.32% and 89.53%, respectively. The stable generalization ability of this method is proven, providing a new research paradigm for automatic emotion recognition. Full article

The emergence of AI companion robots is transforming the landscape of elderly care, offering numerous conveniences to senior citizens when their children are not around. This trend is particularly pertinent in ageing societies such as China. Against this backdrop, the present study aims to explore the acceptance of AI companion robots among the elderly from a user-centric perspective. By leveraging insights from existing studies in the literature, we identified three individual characteristic variables—technology optimism, innovativeness, and familiarity—to extend the Artificial Intelligence Device Use Acceptance (AIDUA) model. Subsequently, we developed a conceptual model which was empirically tested through structural equation modelling (SEM) analysis. Our dataset comprised responses from 452 elderly individuals in China. The results revealed that technology optimism and innovativeness were positively associated with performance expectancy and effort expectancy, whereas familiarity inversely predicted perceived risk. Furthermore, emotion was found to be positively influenced by performance expectancy and effort expectancy but negatively impacted by perceived risk. This research extends the AIDUA model within the context of AI companion robots by integrating individual characteristic variables. These findings offer valuable insights for the design and development of companion robots and enrich the domain of Human–Robot Interaction (HRI). Full article

The accelerated advancement of artificial intelligence (AI) has positioned it as a novel colleague. However, as employees collaborate with AI colleagues in daily work, their communication and interaction with human colleagues may decrease. This may result in feelings of loneliness and a potential reduction in emotional resources, potentially leading to counterproductive work behavior (CWB). Drawing from the conservation of resources (COR) theory, we hypothesize that employee–AI collaboration may amplify employees’ CWB due to loneliness and emotional fatigue. The potential mitigating effects of leader emotional support on these outcomes are also considered. To test these hypotheses, a 2 × 2 vignette experiment (N = 167) was conducted. The results demonstrate that employee–AI collaboration exerts a substantial positive influence on loneliness. Loneliness further increases employees’ emotional fatigue, which in turn increases CWB. Leader emotional support—the care and motivation demonstrated by leaders has been identified as a key factor in reducing loneliness. This research contributes to the extant literature on employee–AI collaboration and CWB, and expands the application scope of COR. Practical implications arise for managers, who are encouraged to consider the impact of employee–AI collaboration on interpersonal interaction and to address employees’ emotional needs in a timely manner. Full article