Search Results (44)

User-generated content, such as product and app reviews, offers more than just sentiment. It provides a rich spectrum of emotional expression that reveals users’ experiences, frustrations and expectations. Traditional sentiment analysis, which typically classifies text as positive or negative, lacks the nuance needed to fully understand the emotional drivers behind customer feedback. In this research, we focus on fine-grained emotion classification using core emotions. By identifying specific emotions rather than sentiment polarity, we enable more actionable insights for e-commerce and app development, supporting strategies such as feature refinement, marketing personalization and proactive customer engagement. We leverage the Hugging Face Emotions dataset and adopt a two-phase modeling approach. In the first phase, we use a pre-trained DistilBERT model as a feature extractor and evaluate multiple classical classifiers (Logistic Regression, Support Vector Classifier, Random Forest) to establish performance baselines. In the second phase, we fine-tune the DistilBERT model end-to-end using the Hugging Face Trainer API, optimizing classification performance through task-specific adaptation. Training is tracked using the Weights & Biases (wandb) API. Comparative analysis highlights the substantial performance gains from fine-tuning, particularly in capturing informal or noisy language typical in user reviews. The final fine-tuned model is applied to a dataset of customers’ reviews, identifying the dominant emotions expressed. Our results demonstrate the practical value of emotion-aware analytics in uncovering the underlying “why” behind user sentiment, enabling more empathetic decision-making across product design, customer support and user experience (UX) strategy. Full article

The absence of parental presence has a direct impact on the emotional stability and social routines of children, especially during extended periods of separation from their family environment, as in the case of daycare centers, hospitals, or when they remain alone at home. At the same time, the technology currently available to provide emotional support in these contexts remains limited. In response to the growing need for emotional support and companionship in child care, this project proposes the development of a multi-platform software architecture based on artificial intelligence (AI), designed to be integrated into humanoid robots that assist children between the ages of 6 and 14. The system enables daily verbal and non-verbal interactions intended to foster a sense of presence and personalized connection through conversations, games, and empathetic gestures. Built on the Robot Operating System (ROS), the software incorporates modular components for voice command processing, real-time facial expression generation, and joint movement control. These modules allow the robot to hold natural conversations, display dynamic facial expressions on its LCD (Liquid Crystal Display) screen, and synchronize gestures with spoken responses. Additionally, a graphical interface enhances the coherence between dialogue and movement, thereby improving the quality of human–robot interaction. Initial evaluations conducted in controlled environments assessed the system’s fluency, responsiveness, and expressive behavior. Subsequently, it was implemented in a pediatric hospital in Guayaquil, Ecuador, where it accompanied children during their recovery. It was observed that this type of artificial intelligence-based software, can significantly enhance the experience of children, opening promising opportunities for its application in clinical, educational, recreational, and other child-centered settings. Full article

Background: This systematic review aims to summarize the most recent data from the literature on the psychological aspects of parents of children prenatally diagnosed with congenital heart defects (CHDs). Methods: A comprehensive literature search was conducted to identify relevant studies on the psychological issues faced by parents of children prenatally diagnosed with CHD. Searches were performed in multiple scientific databases, including PubMed, Science direct, Embase, Scopus, Medline, Clarivate, to ensure the broad coverage of the literature. The search was limited to studies published up until February 2025. The search strategy included the following terms and combinations: “congenital heart defect” OR “CHD” AND “prenatal diagnosis” AND “psychological impact” OR “parental distress” OR “coping”. Results: Eighteen studies involving the 673 parents of fetuses diagnosed with congenital heart defects were included. Studies spanned four continents and employed both qualitative (n = 14) and quantitative (n = 4) designs. Key psychological outcomes reported were anxiety, depression, stress, post-traumatic stress, coping strategies, maternal–fetal attachment, and life satisfaction. Anxiety and depression were the most frequent issues, with maternal anxiety reaching 65% and depression up to 45.7%. Stress related to diagnostic uncertainty was common. While some parents used adaptive coping (social support, emotional regulation), others experienced maladaptive patterns such as avoidance. One study reported increased maternal–fetal attachment following prenatal CHD diagnosis. Predictors of psychological distress included time of diagnosis, parental gender, education level, social support, and severity of the defect. Recommended interventions included early psychological screening, empathetic communication, structured counseling, and long-term emotional support. Despite heterogeneity in design and moderate overall bias, findings highlight a consistent psychological burden among parents, underscoring the need for integrated psychosocial care following a prenatal CHD diagnosis. Conclusions: Parents whose children have been prenatally diagnosed with a congenital heart defect are at an increased risk for psychological distress. To improve the quality of care, a multidisciplinary team is needed to provide parents with the necessary information on diagnosis, interventions, and potential outcomes. Full article

As large language model (LLM) tutors evolve from scripted helpers into adaptive educational partners, their capacity for self-regulation, ethical decision-making, and internal monitoring will become increasingly critical. This paper introduces the Needs-Driven Consciousness Framework (NDCF) as a novel, integrative architecture that combines Dennett’s multiple drafts model, Damasio’s somatic marker hypothesis, and Tulving’s tripartite memory system into a unified motivational design for synthetic consciousness. The NDCF defines three core regulators, specifically Survive (system stability and safety), Thrive (autonomy, competence, relatedness), and Excel (creativity, ethical reasoning, long-term purpose). In addition, there is a proposed supervisory Protect layer that detects value drift and overrides unsafe behaviours. The core regulators compute internal need satisfaction states and urgency gradients, feeding into a softmax-based control system for context-sensitive action selection. The framework proposes measurable internal signals (e.g., utility gradients, conflict intensity Ω), behavioural signatures (e.g., metacognitive prompts, pedagogical shifts), and three falsifiable predictions for educational AI testbeds. By embedding these layered needs directly into AI governance, the NDCF offers (i) a psychologically and biologically grounded model of emergent machine consciousness, (ii) a practical approach to building empathetic, self-regulating AI tutors, and (iii) a testable platform for comparing competing consciousness theories through implementation. Ultimately, the NDCF provides a path toward the development of AI tutors that are capable of transparent reasoning, dynamic adaptation, and meaningful human-like relationships, while maintaining safety, ethical coherence, and long-term alignment with human well-being. Full article

This study aims to understand the meaning nursing professionals attribute to their lived experiences of providing humanized care within a public hospital setting. Grounded in Jean Watson’s theory of human caring, the research adopts a qualitative, descriptive phenomenological design to capture the perceptions and emotions of nurses regarding humanized care. Data were collected through semi-structured interviews with nine experienced nurses, selected through purposive sampling. The interviews, conducted virtually between July and December 2024, were analyzed using Colaizzi’s method and supported by Atlas.ti software. Four main thematic categories emerged: institutional health policies, professional image and identity, strengths and challenges in care, and essential competencies for humanized care. The findings highlight the critical role of empathy, cultural sensitivity, ethical commitment, and emotional presence in delivering compassionate care. Participants emphasized that, beyond clinical procedures, humanized care requires relational and contextual sensitivity, often hindered by institutional limitations and excessive administrative burdens. The study concludes that nursing professionals are key agents in promoting ethical, empathetic, and culturally respectful practices that humanize health services. These insights offer valuable contributions for designing policies and training strategies aimed at strengthening humanized care as a cornerstone of quality healthcare systems. Full article

The modern workplace grapples with a human energy crisis, characterized by chronic exhaustion, disengagement, and emotional depletion among employees. Traditional well-being initiatives often fail to address this systemic challenge, particularly in industrial contexts. This study introduces the Human Energy Management System (HEMS), a strategic framework to develop, implement, and refine strategies for optimizing workforce energy. Grounded in Industry 5.0’s human-centric, resilient, and sustainable principles, HEMS integrates enterprise risk management (ERM), design thinking, and the Plan-Do-Check-Act (PDCA) cycle. Employing a qualitative Design Science Research (DSR) methodology, the study reframes human energy depletion as an organizational risk, providing a proactive, empathetic, and iterative approach to mitigate workplace stressors. The HEMS framework is developed and evaluated through theoretical modeling, literature benchmarking, and secondary case studies, rather than empirical testing, aligning with DSR’s focus on conceptual validation. Findings suggest HEMS offers a robust tool to operationalize human energy reinforcement strategies in industrial settings. Consistent with the European Union’s vision for human-centric industrial transformation, HEMS enables organizations to foster a resilient, engaged, and thriving workforce in both stable and challenging times. Full article

Background/Objectives: Currently, older adults face significant digital exclusion due to a lack of technological skills, which limits their access to essential services and their social participation in an environment increasingly dependent on technology. This study aimed to analyze how technological anxiety and social influence affect the perceived usefulness, perceived ease of use, and adoption intention of technological tools among older adults in Mexico City using the Technology Acceptance Model (TAM). Methods: A survey was conducted with 70 older adults attending an event in Mexico City. Results: The findings confirm that, although perceived usefulness and ease of use remain pillars of technology use intention, technology anxiety acts as a critical barrier limiting adoption. At the same time, social influence has a dual effect: on the one hand, it facilitates the perception of ease of use; on the other, it diminishes the perception of usefulness when support becomes pressuring or impatient. Conclusions: These results underscore the need to design interventions that reduce anxiety, strengthen digital literacy, and promote empathetic and motivating social support, thereby effectively enhancing technology adoption among older adults. Full article

This study uses generative AI to investigate the influence of building façade geometry on human physiological and psychological health. Employing Christopher Alexander’s fifteen fundamental properties of living geometry and a set of ten emotional descriptors {beauty, calmness, coherence, comfort, empathy, intimacy, reassurance, relaxation, visual pleasure, well-being} in separate tests, ChatGPT 4.5 evaluates simple, contrasting window designs. AI analyses strongly and consistently prefer traditional window geometries, characterized by symmetrical arrangements and coherent visual structure, over fragmented or minimalist–modernist alternatives. These results suggest human cognitive–emotional responses to architectural forms are hardwired through evolution, privileging specific geometric patterns. Finally, ChatGPT o3 formulates ten detailed geometric rules for empathetic window design and composition. It then applies these criteria to select contemporary window typologies that generate the highest anxiety. The seven most anxiety-inducing designs are the most favored today worldwide. The findings challenge contemporary architectural preferences and standard window archetypes by emphasizing the significance of empathetic and health-promoting façade designs. Given the general suspicion among many readers of the frequently manipulative and unreliable use of AI, its use in this experiment is not to validate design decisions directly, which would put into question what the AI is trained with, but to prove a correlation between two established methodologies for evaluating a design. AI is used as an analytical tool to show that Alexander’s geometric rules (the guidelines proposed beforehand) closely match emotional reactions (the desirable outcomes observed afterward). This novel use of AI suggests integrating neurodesign principles into architectural education and practice to prioritize urban vitality through psychological well-being. Full article

Background: Stress affects 45% of NHS staff. More research is needed to explore how to develop resilient mental health nurses who face multiple workplace stressors, including interacting with distressed clients. Higher Education Institutions are uniquely placed to introduce coping skills that help reduce anxiety and increase confidence for pre-registration nurses entering placements for the first time. Methods: A convenience sample of first year mental health student nurses (whole cohort), recruited before their first clinical placement, were invited to participate. Following a mixed methods design, we developed a 360-degree virtual reality (VR) video, depicting a distressed service user across three scenes, filmed in a real-life decommissioned in-patient ward. Participants followed the service user through the scenes, as though in real life. We used the video alongside a cognitive reappraisal/solution-focused/VERA worksheet and supportive clinical supervision technique to explore students’ experiences of VR as an educative tool and to help build emotional coping skills. Results: N = 21 mental health student nurses were recruited to the study. Behavioural responses to the distressed patient scenario were varied. Students that had prior experience in health work were more likely to feel detached from the distress of the service user. Although for some students VR provided a meaningful learning experience in developing emotional awareness, other students felt more like a ‘fly on the wall’ than an active participant. Empathetic and compassionate responses were strongest in those who perceived a strong immersive effect. Overall, the supportive supervision appeared to decrease the anxiety of the small sample involved, but confidence was not affected. Conclusion: The use of 360-degree VR technology as an educative, classroom-based tool to moderate anxiety and build confidence in pre-placement mental health nursing students was partially supported by this study. The effectiveness of such technology appeared to be dependent on the degree to which ‘immersion’ and a sense of presence were experienced by students. Our cognitive reappraisal intervention proved useful in reducing anxiety caused by ‘the patient in distress scenario’ but only for students who achieved a deep immersive effect. Students with prior exposure to distressing events (in their personal lives and in clinical settings) might have developed other coping mechanisms (e.g., detachment). These findings support the idea that ‘presence’ is a subjective VR experience and can vary among users. Full article

This study explores the information–communication discourse in modern recruitment by applying the Design Thinking (DT) methodology to enhance employee selection and integration strategies. By incorporating digital tools and empathetic approaches, this study examines innovative practices that improve candidate experience and ensure alignment with organizational culture. This study follows the DT framework, encompassing empathy, problem definition, and ideation, with a research sample including candidates, employees, and HR professionals. Methods such as desk research, interviews, diary methods, and P/C matrix diagonalization, supported by original metrics, assess the effectiveness of these approaches. The findings highlight that digital tools, particularly gamification and online assessments, significantly enhance recruitment quality, increase efficiency, reduce hiring time, and improve cultural alignment. Additionally, this study develops informational constructs of knowledge, skills, and attitudes, offering deeper insights into key factors for successful hiring. By integrating new media and technological solutions, this research contributes to transforming traditional recruitment practices into more candidate-centred processes. Further evaluation through complementary studies is recommended to determine the long-term impact of digital tools on recruitment outcomes and employee selection success. Full article

Background/Objectives: With advancements in Large Language Models (LLMs), counseling chatbots are becoming essential tools for delivering scalable and accessible mental health support. Traditional evaluation scales, however, fail to adequately capture the sophisticated capabilities of these systems, such as personalized interactions, empathetic responses, and memory retention. This study aims to design a robust and comprehensive evaluation scale, the Comprehensive Evaluation Scale for LLM-Powered Counseling Chatbots (CES-LCC), using the eDelphi method to address this gap. Methods: A panel of 16 experts in psychology, artificial intelligence, human-computer interaction, and digital therapeutics participated in two iterative eDelphi rounds. The process focused on refining dimensions and items based on qualitative and quantitative feedback. Initial validation, conducted after assembling the final version of the scale, involved 49 participants using the CES-LCC to evaluate an LLM-powered chatbot delivering Self-Help Plus (SH+), an Acceptance and Commitment Therapy-based intervention for stress management. Results: The final version of the CES-LCC features 27 items grouped into nine dimensions: Understanding Requests, Providing Helpful Information, Clarity and Relevance of Responses, Language Quality, Trust, Emotional Support, Guidance and Direction, Memory, and Overall Satisfaction. Initial real-world validation revealed high internal consistency (Cronbach’s alpha = 0.94), although minor adjustments are required for specific dimensions, such as Clarity and Relevance of Responses. Conclusions: The CES-LCC fills a critical gap in the evaluation of LLM-powered counseling chatbots, offering a standardized tool for assessing their multifaceted capabilities. While preliminary results are promising, further research is needed to validate the scale across diverse populations and settings. Full article

Children with severe motor and communication impairments (SMCIs) face significant challenges in expressing emotions, often leading to unmet needs and social isolation. This study investigated the potential of machine learning to identify emotions in children with SMCIs through the analysis of physiological signals. A model was created based on the data from the DEAP online dataset to identify the emotions of typically developing (TD) participants. The DEAP model was then adapted for use by participants with SMCIs using data collected within the Building and Designing Assistive Technology Lab (BDAT). Key adaptations to the DEAP model resulted in the exclusion of respiratory signals, a reduction in wavelet levels, and the analysis of shorter-duration data segments to enhance the model’s applicability. The adapted SMCI model demonstrated an accuracy comparable to the DEAP model, performing better than chance in TD populations and showing promise for adaptation to SMCI contexts. The models were not reliable for the effective identification of emotions; however, these findings highlight the feasibility of using machine learning to bridge communication gaps for children with SMCIs, enabling better emotional understanding. Future efforts should focus on expanding the data collection of physiological signals for diverse populations and developing personalized models to account for individual differences. This study underscores the importance of collecting data from populations with SMCIs for the development of inclusive technologies to promote empathetic care and enhance the quality of life of children with communication difficulties. Full article

In today’s dynamic organizational environments, interpersonal conflict and social exclusion can significantly impact employee behavior and organizational effectiveness. This study explores the complex interplay between interpersonal conflict, workplace ostracism, and interpersonal deviance in Jordan’s public sector, emphasizing the moderating role of supervisors’ active empathic listening. Using the stressor–emotion model, conservation of resources (COR) theory, and conflict expression (CE) framework, this study examined these relationships through a two-wave survey design. Data were collected from 501 public sector employees using validated scales, and an analysis was conducted using SPSS and AMOS, with structural equation modeling employed for hypothesis testing. The findings reveal that interpersonal conflict strongly predicts workplace ostracism and interpersonal deviance. Workplace ostracism mediates the relationship between conflict and deviance, while supervisors’ active empathic listening moderates these effects, reducing the likelihood of deviant behaviors. These results underscore the importance of fostering empathetic leadership and inclusive workplace environments to mitigate conflict’s negative impact. This research contributes to understanding workplace dynamics by highlighting the critical role of supervisors in moderating conflict and ostracism. The findings have practical implications for public sector organizations. Beyond training programs, supervisors can implement active empathic listening in practical settings by regularly holding one-on-one meetings in which they actively listen to employee concerns, using verbal and non-verbal cues to show engagement, asking open-ended questions to encourage deeper discussion, reflecting employee emotions to validate their feelings, and following up on issues raised to demonstrate concrete action based on what they have heard; this creates a culture of open communication in which employees feel heard and valued, leading to increased employee engagement and improved problem-solving abilities. Full article

The architecture in the title refers to physical buildings, spaces, and walls. Dominant architectural culture prefers minimalist environments that contradict the information setting needed for the infant brain to develop. Much of world architecture after World War II is therefore unsuitable for raising children. Data collected by technological tools, including those that use AI for processing signals, indicate a basic misfit between cognition and design. Results from the way AI software works in general, together with mobile robotics and neuroscience, back up this conclusion. There exists a critical research gap: the systematic investigation of how the geometry of the built environment influences cognitive development and human neurophysiology. While previous studies have explored environmental effects on health (other than from pathogens and pollutants), they largely focus on factors such as acoustics, color, and light, neglecting the fundamental role of spatial geometry. Geometrical features in the ancestral setting shaped neural circuits that determine human cognition and intelligence. However, the contemporary built environment consisting of raw concrete, plate glass, and exposed steel sharply contrasts with natural geometries. Traditional and vernacular architectures are appropriate for life, whereas new buildings and urban spaces adapt to human biology and are better for raising children only if they follow living geometry, which represents natural patterns such as fractals and nested symmetries. This study provides a novel, evidence-based framework for adaptive and empathetic architectural design. Full article

Simulation-Based Learning (SBL) in education has demonstrated significant potential in preparing participants to effectively address future challenges in a dynamic and ever-changing world. Empathy, as a multidimensional skill, is fundamental to successfully navigate these complex situations. This study aims to assess the effectiveness of SBL in enhancing student teachers’ awareness and understanding of empathy’s multifaceted nature. Using a quasi-experimental design, 232 students participated in courses that integrated empathy instruction with simulation workshop experiences. The students used a rubric based on the Empathetic Patterns in Interpersonal Communication (EPIC) model, developed and validated by the authors in prior studies, to identify empathic patterns in two videotaped simulations shown to them at the beginning and end of the course. The same task was completed by seven content experts in empathy and psychology, as well as six experienced simulation workshop instructors serving as clinical experts. Additionally, the students responded to open-ended questions suggesting various expressions of empathy. The results indicated that SBL workshops, when integrated into a teaching framework that addresses empathy and analyzed through a structured rubric, can serve as an effective platform for enhancing students’ ability to identify and understand empathic patterns. Full article