Search Results (415)

The scientific study of emotion remains fraught with conceptual ambiguity, methodological limitations, and epistemological blind spots. This theoretical paper argues that existing paradigms frequently capture synthetic rather than natural emotional states—those shaped by social expectations, cognitive scripting, and performance under observation. We propose a conceptual framework that distinguishes natural emotion—spontaneous, embodied, and interoceptively grounded—from synthetic forms that are adaptive, context-driven, and often unconsciously rehearsed. These reactions often involve emotional scripts rather than genuine, spontaneous affective experiences. Drawing on insights from affective neuroscience, psychological measurement, artificial intelligence, and neurodiversity, we examine how widely used tools such as EEG, polygraphy, and self-report instruments may capture emotional conformity rather than authenticity. We further explore how affective AI systems trained on socially filtered datasets risk replicating emotional performance rather than emotional truth. By recognizing neurodivergent expression as a potential site of emotional transparency, we challenge dominant models of emotional normalcy and propose a five-step agenda for reorienting emotion research toward authenticity, ecological validity, and inclusivity. This post-synthetic framework invites a redefinition of emotion that is conceptually rigorous, methodologically nuanced, and ethically inclusive of human affective diversity. Full article

Brain tumors elicit complex neuropsychiatric disturbances that frequently occur prior to radiological detection and hinder differentiation from major psychiatric disorders. These syndromes stem from tumor-dependent metabolic reprogramming, neuroimmune activation, neurotransmitter dysregulation, and large-scale circuit disruption. Dinucleotide hypermethylation (e.g., IDH-mutant gliomas), through the accumulation of 2-hydroxyglutarate (2-HG), execute broad DNA and histone hypermethylation, hypermethylating serotonergic and glutamatergic pathways, and contributing to a treatment-resistant cognitive-affective syndrome. High-grade gliomas promote glutamate excitotoxicity via system Xc⁻ transporter upregulation that contributes to cognitive and affective instability. Cytokine cascades induced by tumors (e.g., IL-6, TNF-α, IFN-γ) lead to the breakdown of the blood–brain barrier (BBB), which is thought to amplify neuroinflammatory processes similar to those seen in schizophrenia spectrum disorders and autoimmune encephalopathies. Frontal gliomas present with apathy and disinhibition, and temporal tumors lead to hallucinations, emotional lability, and episodic memory dysfunction. Tumor-associated neuropsychiatric dysfunction, despite increasing recognition, is underdiagnosed and commonly misdiagnosed. This paper seeks to consolidate the mechanistic understanding of these syndromes, drawing on perspectives from neuroimaging, molecular oncology, neuroimmunology, and computational psychiatry. Novel approaches, including lesion-network mapping, exosomal biomarkers or AI-based predictive modeling, have projected early detection and precision-targeted interventions. In the context of the limitations of conventional psychotropic treatments, mechanistically informed therapies, including neuromodulation, neuroimmune-based interventions, and metabolic reprogramming, are essential to improving psychiatric and oncological outcomes. Paraneoplastic neuropsychiatric syndromes are not due to a secondary effect, rather, they are manifestations integral to the biology of a tumor, so they require a new paradigm in both diagnosis and treatment. And defining their molecular and circuit-level underpinnings will propel the next frontier of precision psychiatry in neuro-oncology, cementing the understanding that psychiatric dysfunction is a core influencer of survival, resilience, and quality of life. Full article

Artificial Intelligence (AI) profoundly shapes adolescents’ digital experiences, presenting both developmental opportunities and risks related to privacy and psychological well-being. This study investigates first the possible generational gap between adolescents and their parents in AI use and trust, and then the associations between the Trait Emotional Intelligence (trait EI), parenting styles, perceived social support, and parental involvement on adolescents’ use and trust in AI-based technologies. Participants were 170 adolescents (aged 13–17) and 175 parents from southern Italy, who completed standardized questionnaires assessing parenting styles, Trait Emotional Intelligence (Trait EI), social support, digital literacy, and use and trust in AI. Adolescents used AI more frequently than parents, especially for school- or work-related support and were more likely to seek behavioral advice from AI. They also showed higher trust in AI data security and the quality of behavioral advice than parents. Moreover, greater trait EI and more authoritative (vs. authoritarian) parenting were associated with less frequent AI use and lower use and trust in AI. In 47 matched parent–adolescent dyads, cluster analysis identified Balanced Users (higher trait EI, authoritative parenting, stronger support, cautious AI use) and At-Risk Users (lower trait EI, authoritarian parenting, lower support, heavier and more trusting AI use) Despite no causal inferences can be drawn due to the correlational nature of the data, the results suggested the importance of considering adolescents’ trait EI and authoritative parenting practices in supporting balanced and critical digital engagement, highlighting the concept of a “digital secure base” as essential for navigating the evolving digital landscape. Full article

The integration of Artificial Intelligence (AI) in education has raised important questions about its role in supporting inclusive practices, particularly in special education. This qualitative-dominant study with quantitative support examines how special education teachers in inclusive primary classrooms in Greece use ChatGPT to design Individualized Education Programs (IEPs) for students with learning disabilities. Six teachers participated, with some employing ChatGPT and others relying on traditional methods. The quality of IEP goals was described using the Revised IEP/IFSP Goals and Objectives Rating Instrument (R-GORI), while in-depth teacher perspectives were explored through thematic analysis. Findings suggest that ChatGPT contributed to clearer goal-setting, generation of diverse instructional resources, and more structured lesson planning. However, teachers emphasized the need for critical oversight, adaptation to real-world classroom conditions, and safeguarding the relational and emotional aspects of teaching. Participants expressed cautious optimism, viewing ChatGPT as a valuable support tool when integrated thoughtfully and ethically. These context-specific, exploratory results offer preliminary guidance for educators, policymakers, and researchers seeking to integrate AI tools into special education. They highlight the importance of targeted professional development, ethical safeguards, and further large-scale research to evaluate the broader applicability of AI-assisted IEP planning. Full article

Facial emotion recognition has become increasingly important in healthcare, where understanding delicate cues like pain, discomfort, or unconsciousness can support more timely and responsive care. Yet, recognizing facial expressions in real-world settings remains challenging due to varying lighting, facial occlusions, and hardware limitations in clinical environments. To address this, we propose TriViT-Lite, a lightweight yet powerful model that blends three complementary components: MobileNet, for capturing fine-grained local features efficiently; Vision Transformers (ViT), for modeling global facial patterns; and handcrafted texture descriptors, such as Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG), for added robustness. These multi-scale features are brought together through a texture-aware cross-attention fusion mechanism that helps the model focus on the most relevant facial regions dynamically. TriViT-Lite is evaluated on both benchmark datasets (FER2013, AffectNet) and a custom healthcare-oriented dataset covering seven critical emotional states, including pain and unconsciousness. It achieves a competitive accuracy of 91.8% on FER2013 and of 87.5% on the custom dataset while maintaining real-time performance (~15 FPS) on resource-constrained edge devices. Our results show that TriViT-Lite offers a practical and accurate solution for real-time emotion recognition, particularly in healthcare settings. It strikes a balance between performance, interpretability, and efficiency, making it a strong candidate for machine-learning-driven pattern recognition in patient-monitoring applications. Full article

Post-Traumatic Stress Disorder (PTSD) poses complex clinical challenges due to its emotional volatility, contextual sensitivity, and need for personalized care. Conventional AI systems often fall short in therapeutic contexts due to lack of explainability, ethical safeguards, and narrative understanding. We propose a hybrid neuro-symbolic architecture that combines Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), symbolic controllers, and ensemble classifiers to support clinicians in PTSD follow-up. The proposal integrates real-time anonymization, session memory through patient-specific RAG, and a Human-in-the-Loop (HITL) interface. It ensures clinical safety via symbolic logic rules derived from trauma-informed protocols. The proposed architecture enables safe, personalized AI-driven responses by combining statistical language modeling with explicit therapeutic constraints. Through modular integration, it supports affective signal adaptation, longitudinal memory, and ethical traceability. A comparative evaluation against state-of-the-art approaches highlights improvements in contextual alignment, privacy protection, and clinician supervision. Full article

This research presents a generative AI mechanism designed to assist artists in finding inspiration and developing ideas during their creative process by leveraging their emotions as a driving force. The proposed iterative inspiration cycle, complete with feedback loops, helps artists digitally capture their creative emotions and use them as a guiding “vision” for creating artwork. Within the mechanism, the “Emotion Vision” images, generated from sketch line drawings and creative emotion prompts, are a medium designed to inspire artists. Experimental results demonstrate a positive inspirational effect, particularly in the creation of ‘Abstract Expressionism’ and ‘Impressionism’ artworks. In addition, we introduce the Emotion Vision Score metric, which quantifies the effectiveness of emotional inspiration. This metric evaluates how well “Emotion Vision” images inspire artists by balancing sketch intentions, creative emotions, and inspirational diversity, thus identifying the most effective images for inspiration. This novel mechanism integrates emotional intelligence into AI for art creation, allowing it to understand and replicate human emotion in its outputs. By enhancing emotional depth and ensuring consistency in generative AI, this research aims to advance digital art creation and contribute to the evolution of artistic expression through generative AI. Full article

Amid the nationwide promotion of fitness and the rapid expansion of China’s sports industry, enhancing spectator satisfaction in sports consumption has become a crucial driver for the industry’s sustainable development. Based on the theory of mind-flow perception, this paper explores the influence of stadium intelligent design on race consumption satisfaction, focusing on the four dimensions of stadium intelligent application perception, personality design perception, digital development perception, and technology integration perception, introduces the mind-flow experience as a mediating variable to construct a theoretical model, and analyzes the questionnaire data of 641 spectators with structural equation modeling. The results show that each perception dimension of intelligent design of stadiums has a significant positive effect on consumer satisfaction. Among them, intelligent applications enhance convenience and interactivity, individual design stimulates emotional resonance and immersion, and digital development and technological convergence optimize the audience’s interactive experience through augmented reality, the Internet of Things, and other technologies. flow experience serves as a key mediator to transform functional attributes into emotional value and immersion experience, significantly enhancing satisfaction. This study contributes theoretical insights and managerial guidance for the integration of AI-driven design, human–technology interaction, and smart construction strategies in modern sports venues. The results have broader implications for enhancing digital user environments and optimizing the infrastructure for next-generation event-based urban development. Full article

Artificial intelligence (AI) soft skills have become increasingly vital in today’s technology-driven world, as they support decision-making systems, strengthen collaboration among stakeholders, and enable individuals to adapt to rapidly changing environments—factors that are fundamental for achieving the sustainability goals of smart cities. Even though AI soft skills are becoming more important, no scale specifically designed to identify and evaluate individuals’ AI soft skills has been found in the existing literature. Therefore, this paper aimed to develop a reliable and valid scale to identify the AI soft skills of individuals. A sample of 685 individuals who were employed in AI-active sectors, with a minimum of a bachelor’s degree, and at least one year of AI-related work experience, participated in the study. A sequential exploratory mixed-methods research design was utilized. Exploratory factor analysis (EFA) identified a five-factor structure that accounted for 67.37% of the total variation, including persuasion, collaboration, adaptability, emotional intelligence, and creativity. Factor loadings ranged from 0.621 to 0.893, and communalities ranged from 0.587 to 0.875. Confirmatory factor analysis (CFA) supported this structure, with strong model fit indices (GFI = 0.940, AGFI = 0.947, NFI = 0.949, PNFI = 0.833, PGFI = 0.823, TLI = 0.972, IFI = 0.975, CFI = 0.975, RMSEA = 0.052, SRMR = 0.035). Internal consistency for each factor was high, with Cronbach’s alpha values of dimensions ranging from 0.804 to 0.875, with a value of 0.921 for the overall scale. Convergent and discriminant validity analyses further confirmed the construct’s robustness. The finalized AI soft skills (AISS) scale, consisting of 24 items, offers a psychometrically valid and reliable tool for assessing essential AI soft skills in professional contexts. Ultimately, this developed scale enables the determination of the social and cognitive skills needed in the human-centered and participatory governance structures of smart cities, supporting the achievement of specific Sustainable Development Goals such as SDG 4, SDG 8, and SDG 11, and contributes to the design of policies and training programs to eliminate the deficiencies of individuals in these areas. Thus, it becomes possible to create qualified human resources that support sustainable development in smart cities, and for these individuals to take an active part in the labor market. Full article

Children’s physical and mental wellbeing is declining, partly due to reduced independent mobility and lack of engaging public environments. This study explores a co-design approach in which children actively participated in a series of design workshops focused on improving school routes through sensory engagement and imagination. Using sensory walks, students mapped positive and negative experiences in their everyday surroundings. Through hands-on creative exercises and the integration of AI and VR tools, they developed design proposals envisioning safer, more enjoyable, and inclusive mobility environments. The findings reveal that while children are highly capable of generating creative and context-sensitive ideas, they are less accustomed to reflecting on sensory input beyond vision. The results underscore the importance of designing urban spaces that prioritize safety, playfulness, and multisensory richness, with particular emphasis on nature and emotional connection. Full article

Fake news generation and propagation occurs in large volumes, at high speed, in diverse formats, while also being short-lived to evade detection and counteraction. Despite its role as an enabler, Artificial Intelligence (AI) has been effective at fake news detection and prediction through diverse techniques of both supervised and unsupervised machine learning. In this article, we propose a novel Artificial Intelligence (AI) approach that addresses the underexplored attribution of information asymmetry in fake news detection. This approach demonstrates how fine-tuned language models and emotion embeddings can be used to detect information asymmetry in intent, emotional framing, and linguistic complexity between content creators and content consumers. The intensity and temperature of emotion, selection of words, and the structure and relationship between words contribute to detecting this asymmetry. An empirical evaluation conducted on five benchmark datasets demonstrates the generalizability and real-time detection capabilities of the proposed AI approach. Full article

Background/Objectives: Radiology often presents communication challenges due to its technical complexity, particularly for patients, trainees, and non-specialist clinicians. This study aims to evaluate the effectiveness of RadioBot, an AI-powered chatbot developed on the Botpress platform, in enhancing radiological communication through natural language processing (NLP). Methods: RadioBot was designed to provide context-sensitive responses based on guidelines from the American College of Radiology (ACR) and the Radiological Society of North America (RSNA). It addresses queries related to imaging indications, contraindications, preparation, and post-procedural care. A structured evaluation was conducted with twelve participants—patients, residents, and radiologists—who assessed the chatbot using a standardized quality and satisfaction scale. Results: The chatbot received high satisfaction scores, particularly from patients (mean = 4.425) and residents (mean = 4.250), while radiologists provided more critical feedback (mean = 3.775). Users appreciated the system’s clarity, accessibility, and its role in reducing informational bottlenecks. The perceived usefulness of the chatbot inversely correlated with the user’s level of expertise, serving as an educational tool for novices and a time-saving reference for experts. Conclusions: RadioBot demonstrates strong potential in improving radiological communication and supporting clinical workflows, especially with patients where it plays an important role in personalized medicine by framing radiology data within each individual’s cognitive and emotional context, which improves understanding and reduces associated diagnostic anxiety. Despite limitations such as occasional contextual incoherence and limited multimodal capabilities, the system effectively disseminates radiological knowledge. Future developments should focus on enhancing personalization based on user specialization and exploring alternative platforms to optimize performance and user experience. Full article

As the global population ages, older adults face growing psychological challenges such as loneliness, cognitive decline, and loss of social roles. Meanwhile, artificial intelligence (AI) technologies, including chatbots and voice-based systems, offer new pathways to emotional support and mental stimulation. However, older adults often encounter significant barriers in accessing and effectively using AI tools. This review examines the current landscape of AI applications aimed at enhancing psychological well-being among older adults, identifies key challenges such as digital literacy and usability, and highlights design and training strategies to bridge the digital divide. Using socioemotional selectivity theory and technology acceptance models as guiding frameworks, we argue that AI—especially in the form of conversational agents—holds transformative potential in reducing isolation and promoting emotional resilience in aging populations. We conclude with recommendations for inclusive design, participatory development, and future interdisciplinary research. Full article

Artificial intelligence (AI) techniques have propelled biomedical sensors beyond measuring physiological markers to interpreting subjective states like stress, pain, or emotions. Despite these technological advances, user trust is not guaranteed and is inadequately addressed in extant research. This review proposes the Cognition–Context–Contrast (CCC) conceptual framework to explain the trust and acceptance of AI-enabled sensors. First, we map cognition, comprising the expectations and stereotypes that humans have about machines. Second, we integrate task context by situating sensor applications along an intellective-to-judgmental continuum and showing how demonstrability predicts tolerance for sensor uncertainty and/or errors. Third, we analyze contrast effects that arise when automated sensing displaces familiar human routines, heightening scrutiny and accelerating rejection if roll-out is abrupt. We then derive practical implications such as enhancing interpretability, tailoring data presentations to task demonstrability, and implementing transitional introduction phases. The framework offers researchers, engineers, and clinicians a structured conceptual framework for designing and implementing the next generation of AI biosensors. Full article

In the era of generative artificial intelligence, writing has evolved into a programmable practice capable of generating sustainable narratives and preserving cultural heritage through poetic prompts. This study proposes “Writing Is Coding ” as a paradigm for sustainability education, exploring how students engage with AI-mediated multimodal creation to address environmental challenges. Using grounded theory methodology with 57 twelfth-grade students from technology-integrated high schools, we analyzed their experiences creating environmental stories and digital cultural artifacts using MidJourney, Kling, and Sora. Data collection involved classroom observations, semi-structured interviews, and reflective journals, analyzed through systematic coding procedures (κ = 0.82). Five central themes emerged: writing as algorithmic design for sustainability (89.5%), emotional scaffolding for environmental awareness (78.9%), aesthetics of imperfection in cultural preservation (71.9%), collaborative dynamics in sustainable creativity (84.2%), and pedagogical value of prompt literacy (91.2%). Findings indicate that AI deepens environmental consciousness and reframes writing as a computational process for addressing global issues. This research contributes a theoretical framework integrating expressive writing with algorithmic thinking in AI-assisted sustainability education, aligned with SDGs 4, 11, and 13. Full article