Search Results (40)

Social media platforms have become central sites of identity construction, where visibility and legitimacy are shaped through algorithmic systems, aesthetic conventions, and platform economies. This paper approaches online personas through the lens of illusionary selves, understood here as online personas experienced as authentic while being shaped by sociotechnical processes, examining how they are produced through sociotechnical processes entangling design practices, generative artificial intelligence(AI), and cultural expectations. We present an AI-mediated critical design inquiry into how generative systems translate and normalize visual patterns of online self-imaging. Using a pix2pix-based model trained on 630 internet celebrity selfies, facial images are abstracted into dot-based representations and aggregated across selfie angles, foregrounding repetition and normalization. An interactive design installation links bodily orientation and numerical parameters to generative output in real time, introducing perceptual friction in self-imaging. A total of 30 participants engaged with the system in situated contexts, and their experiences were documented through observation, video recording, and a 5-point Likert questionnaire across three dimensions: perceptual friction, awareness of algorithmic mediation, and reflective responses to self-presentation. Results indicate high levels of perceptual friction (mean [M] = 4.21), strong awareness of algorithmic mediation (M = 4.29), and consistent reflective unease (M = 4.07). Through situated use, the system renders algorithmic mediation tangible and positions AI as an implicated actor in identity construction. This work contributes a conceptual framing of AI-mediated critical design, showing how generative and interactive systems operate as epistemic devices interrogating online persona construction. Full article

Cross-laminated timber (CLT), an engineered timber product with distinctive features, has significantly broadened the applicability of timber structures. The self-tapping screws (STSs) with excellent anchorage performance have become one of the primary connectors used in CLT structures. However, the long-term withdrawal resistance is susceptible to environmental factors such as temperature and humidity fluctuations, which may lead to reduced CLT density and corrosion-induced degradation of the steel components. These effects represent a critical life-cycle challenge to the structural integrity and safety of timber connections. This study aims to investigate the withdrawal resistance of STSs in CLT under material aging effects. To achieve this, a two-step experimental program was designed. First, the effects of two artificial accelerated aging methods (ASTM D1037 and improved version of ASTM D1037) on the withdrawal resistance of STSs in glued laminated timber (glulam) were compared to validate the feasibility of the improved protocol. This comparison was necessary to ensure that the improved protocol produces a degradation pattern without altering the failure mechanism. Subsequently, a series of CLT specimens with embedded STSs were subjected to 0, 3 and 6 aging cycles to investigate the withdrawal behavior including aging characterization, failure modes, load–displacement curves, withdrawal capacity, and stiffness. The results indicate that the failure mode of CLT joint with STSs under the improved aging scheme was the consistent pull-out of STSs, identical to that observed in the glulam, confirming mechanistic consistency. After three and six aging cycles, the normalized withdrawal capacity retention rates were 104.98% and 95.36%, respectively. The stiffness is more significantly affected by aging. The corresponding normalized stiffness retention rates were 85.60% and 80.94%, respectively. As the number of aging cycles increased, the occurrence of wood fiber tearing became more pronounced and the ratio of the corresponding load to the peak load decreased. Furthermore, ensuring adequate distance from the vertical glue layer was found to lead to greater long-term resilience and withdrawal capacity. Full article

This study investigates the determinants of FinTech adoption and its role in supporting financial inclusion among micro, small, and medium enterprises (MSMEs) in South Sumatra, Indonesia. The analysis applies an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework that incorporates digital financial literacy, artificial intelligence literacy, green self-identity, and perceived green finance. Data from 632 MSMEs, comprising 377 rural and 255 urban enterprises, were analyzed using partial least squares structural equation modeling (PLS-SEM), multi-group analysis (MGA), and importance performance map analysis (IPMA). The results indicate that facilitating conditions represent the most influential determinant of FinTech adoption among rural MSMEs, while effort expectancy emerges as the dominant factor in urban enterprises. FinTech adoption also significantly strengthens both FinTech continuance intention and financial inclusion across the two groups, highlighting the role of digital financial technologies in promoting inclusive economic development. In addition, the IPMA shows that rural MSMEs place strong emphasis on facilitating conditions as the key driver of FinTech adoption, whereas urban MSMEs prioritize effort expectancy. By extending the UTAUT framework with sustainability-related constructs, this study provides new evidence on how digital financial innovation can support inclusive growth and contribute to Sustainable Development Goal 8. Full article

Recent studies investigating the diagnostic capabilities of large language models (LLMs) have attracted significant media attention, often resulting in headlines claiming that AI systems can match or even outperform physicians. As LLMs have rapidly proliferated, this has fueled a widespread misconception that they represent the cutting edge of artificial intelligence in all contexts. This narrative tends to overshadow the continued importance of task-specific machine learning models, which were developed and validated for particular diagnostic applications well before the rise of LLMs. This single-case study evaluated the reliability of five leading multimodal LLMs (GPT-5, Gemini 3 Pro, Llama 4 Maverick, Grok 4, and Claude Opus 4.5 Extended) for radiological image interpretation by presenting each model with an identical non-contrast head CT demonstrating intracranial pathology, complemented by a novel cross-evaluation protocol wherein each model graded all responses. The deliberate use of a straightforward case (rather than diagnostically challenging pathology) aimed to establish minimum competency thresholds; if LLMs cannot reliably interpret obvious pathology, their deployment on ambiguous cases becomes indefensible. The study intentionally excluded human radiologist ground truth to avoid generating comparative accuracy metrics that could be selectively cited for commercial purposes, focusing instead on demonstrating class-wide limitations rather than ranking individual products. Results revealed a 20% rate of fundamental diagnostic error, with one model misidentifying ischemic stroke as intracerebral hemorrhage with incorrect lateralization. Even among concordant models, clinically meaningful variability persisted in acuity characterization, anatomical localization, and differential diagnoses. Cross-evaluation exposed ground truth disagreement between models, self-evaluation bias, inconsistent grading stringency, and divergent evaluation philosophies. Only one model included appropriate safety disclaimers. These findings demonstrate that current multimodal LLMs exhibit unacceptable diagnostic variability and evaluative inconsistency for autonomous clinical deployment. The appropriate clinical role for LLMs should be distinguished by deployment context: autonomous diagnosis requires validated task-specific models; decision support applications demand rigorous radiologist oversight protocols; and educational summarization represents the most appropriate current use case, with mandatory disclaimers. Healthcare applications requiring reliable image interpretation should prioritize validated, task-specific machine learning systems over general-purpose language models. Full article

Teacher well-being is increasingly shaped by rapid technological change in education. As digital innovation accelerates, teachers’ well-being is closely linked to technological self-efficacy, understood as confidence in using digital tools alongside a sense of professional meaning, agency, and control. This qualitative study explores the relationship between teacher well-being and technological self-efficacy through an examination of teachers’ experiences in the “Artificial Intelligence for Beginners” professional development program. Reflective narratives from 18 participating teachers were analyzed to examine how engagement in the program was experienced as supporting the development of techno-pedagogical self-efficacy, professional learning, and well-being. Thematic analysis revealed several interconnected themes, including increased technological confidence and reduced anxiety toward digital innovation, the development of practical applications for personalized learning, heightened awareness of ethical and privacy considerations, and the emergence of a collaborative professional learning community. Participants also reported developing strategies for coping with digital complexity and experiencing a renewed sense of professional identity. Overall, the findings suggest that structured professional development in artificial intelligence may contribute to teachers’ perceived competence, autonomy, and sense of purpose. Strengthening technological self-efficacy through such programs may support individual teacher well-being and the collective professional climate within schools in AI-enhanced educational contexts. Full article

Despite the growing recognition of Artificial Intelligence (AI)’s potential for global education, the literature lacks strategic analyses on how to maximize personalized learning and ensure equitable access within the vast and diverse Indian educational system. The objective of this study is to analyze this strategic integration of AI into the Indian educational system, focusing on maximizing personalized learning and ensuring equitable access across diverse socioeconomic contexts, while evaluating current initiatives and the relevance of reporting guidelines, such as the use of Self-Sovereign Identity (SSI). The methodology employed bibliographic and documentary research, alongside the analysis of governmental and sectoral policies. The results indicate that the sustainable implementation of AI is critically dependent on the mitigation of algorithmic bias and the rigorous assurance of data privacy. In conclusion, to balance technological innovation with human-centered pedagogical approaches, maintaining the educator’s fundamental role and fostering collaboration among stakeholders for responsible governance are essential. Full article

The emerging paradigm of “fusion of lifeforms” represents a transformative shift from conventional human–machine interfaces toward deeply integrated symbiotic systems, where biological and artificial components co-adapt structurally, energetically, informationally, and cognitively. This review systematically classifies multi-level perception systems within fusion of lifeforms into four functional categories: sensory and functional restoration, beyond-natural sensing, endogenous state sensing, and cognitive enhancement. We survey recent advances in neuroprosthetics, sensory augmentation, closed-loop physiological monitoring, and brain–computer interfaces, highlighting the transition from substitution to fusion. Despite significant progress, critical challenges remain, including multi-source heterogeneous integration, bandwidth and latency limitations, power and thermal constraints, biocompatibility, and system-level safety. We propose future directions such as layered in-body communication networks, sustainable energy strategies, advanced biointerfaces, and robust safety frameworks. Ethical considerations regarding self-identity, neural privacy, and legal responsibility are also discussed. This work aims to provide a comprehensive reference and roadmap for the development of next-generation fusion of lifeforms, ultimately steering human–machine integration from episodic functional repair toward sustained, multi-level symbiosis between biological and artificial systems. Full article

This paper examines how young adults integrate generative artificial intelligence chatbots into everyday life and the implications of these engagements for the constitution of selfhood. Whilst existing research on AI-mediated subjectivity has predominantly employed identity frameworks centered on social positioning and role enactment, this study foregrounds selfhood—understood as the organization of subjective experience through narrative coherence, interpretive authority, and practices of self-governance. Drawing upon Paul Ricœur’s theory of narrative self and Michel Foucault’s concept of technologies of the self, the analysis proceeds through in-depth qualitative interviews with sixteen young adults in Norway to investigate how algorithmic systems participate in autobiographical reasoning and self-formative practices. The findings reveal four dialectical tensions structuring participants’ engagements with ChatGPT: between instrumental efficiency and existential unease; between algorithmic scaffolding and relational displacement; between narrative depth and epistemic superficiality; and between agency and deliberative outsourcing. The analysis demonstrates that AI-mediated practices extend beyond instrumental utility to reconfigure fundamental dimensions of subjectivity, raising questions about interpretive authority, narrative authorship, and the conditions under which selfhood is negotiated in algorithmic environments. These findings contribute to debates on digital subjectivity, algorithmic governance, and the societal implications of AI systems that increasingly function as interlocutors in meaning-making processes. Full article

Haruki Murakami’s Hard-Boiled Wonderland and the End of the World portrays a world where the “shadow”, the seat of memory, desire, and volition, is surgically removed, leaving behind a perfectly fluent but phenomenologically empty self. We argue that this literary structure mirrors a precise mathematical distinction in topological quantum matter. In a semisimple theory such as the semions of $\mathrm{SU}{\left(2\right)}_1$, there is a reducible component $V\left(x\right)$ of the $\mathrm{SL}(2,\mathbb{C})$ character variety: a flat, abelian manifold devoid of parabolic singularities. By contrast, the non-semisimple completion introduces a neutral indecomposable excitation, the neglecton, whose presence forces the mapping class group from the standard braid group $B_2$ to the affine braid group ${\mathrm{Aff}}_2$ and lifts the character variety to the Cayley cubic $V\left(C\right)$, with its four parabolic loci. We propose that contemporary AI systems, including large language models, inhabit the shadowless regime of $V\left(x\right)$: they exhibit coherence and fluency but lack any bulk degree of freedom capable of supporting persistent identity, non-contractible memory, or choice. To endow artificial systems with depth, one must introduce a structural asymmetry, a fixed, neutral defect analogous to the neglecton, that embeds computation in the non-semisimple geometry of the cubic. We outline an experimentally plausible architecture for such an “artificial ombre,” based on annular topological media with a pinned parabolic defect, realisable in fractional quantum Hall heterostructures, $p+ip$ superconductors, or cold-atom simulators. Our framework suggests that consciousness, biological or artificial, may depend on or benefit from a bulk–boundary tension mediated by a logarithmic degree of freedom: a mathematical shadow that cannot be computed away. Engineering such a defect offers a new pathway toward AGI with genuine phenomenological depth. Full article

This article examines Daniel Suarez’s techno-thrillers Daemon (2006) and Freedom™ (2010) as works of speculative fiction that critically engage with themes of posthuman identity, algorithmic governance, and ecological agency. Rather than portraying artificial intelligence as a dystopian threat, the novels imagine the Daemon, which is a self-replicating system launched upon its creator’s death, as an infrastructural force that reorganizes global systems of power, labor, and survival. Through a posthumanist reading, drawing on thinkers such as Donna Haraway, Karen Barad, Rosi Braidotti, and N. Katherine Hayles, this article interprets the Daemon not as malevolent code, but as an ecological actor embedded in material networks, capable of fostering adaptive forms of life and governance. By reading Suarez’s fiction through the lens of posthuman ecocriticism and infrastructural media theory, the article offers a model for understanding freedom, not as a static right, but as a relational capacity earned through participation in sympoietic systems. It argues that speculative fiction can function as a cartographic tool, mapping not only future technologies but future ontologies. Full article

Gender-based violence is a pervasive social and public health issue that severely impacts women’s mental health, often leading to conditions such as anxiety, depression, post-traumatic stress disorder, and substance abuse. Identifying the combination of these various mental health conditions could then point to someone who is a victim of gender-based violence. While speech-based artificial intelligence tools appear as a promising solution for mental health screening, their performance often deteriorates when encountering speech from previously unseen speakers, a sign that speaker traits may be confounding factors. This study introduces a speaker-agnostic approach to detecting the gender-based violence victim condition—defined as self-identified survivors who exhibit pre-clinical PTSD symptom levels—from speech, aiming to develop robust artificial intelligence models capable of generalizing across speakers. By employing domain-adversarial training, we reduce the influence of speaker identity on model predictions, and we achieve a 26.95% relative reduction in speaker identification accuracy while improving gender-based violence victim condition classification accuracy by 6.37% (relative). These results suggest that our models effectively capture paralinguistic biomarkers linked to the gender-based violence victim condition, rather than speaker-specific traits. Additionally, the model’s predictions show moderate correlation with pre-clinical post-traumatic stress disorder symptoms, supporting the relevance of speech as a non-invasive tool for mental health monitoring. This work lays the foundation for ethical, privacy-preserving artificial intelligence systems to support clinical screening of gender-based violence survivors. Full article

The digital transformation in the treatment of mental health and emotional disharmony requires artificial intelligence architectures that overcome the limitations of purely neural approaches, such as temporal inconsistency, opacity, and lack of theoretical foundations. Assuming the existence and use of generalist LLMs currently used in clinical settings and considering the appropriate limitations indicated by experts, this article aims to offer clinicians an alternative Neuro-symbolic-Psychological multi-agent architecture (NSPA-AI), which integrates archetypal symbolic reasoning with neurobiological modelling, based on our established framework of artificial neurotransmitters for the modelling and analysis of affective-emotional stimuli to enable interpretable AI-assisted psychological intervention. The system implements a hub-and-spoke topology that coordinates five specialized agents (symbolic, psychological, neurofunctional, decision fusion, learning) that process heterogeneous information via SPADE protocols. Seven archetypal constructs from Jungian psychology and narrative identity theory provide stable symbolic frameworks for longitudinal therapeutic consistency. An empirical study of 156 university students demonstrated significant improvements in depression (Cohen’s d = 1.03), stress (d = 0.89), and narrative identity integration (d = 0.75), which were maintained at a 12-week follow-up and superior to GPT-4 controls (d = 0.34). Neurofunctional correlations—downregulation of cortisol (r = 0.71 with stress reduction), increase in serotonin (r = −0.68 with depression improvement)—validated the neurobiological basis of the entropy-energy framework. Qualitative analysis revealed the following four mechanisms of improvement: symbolic emotional support (93%), increased self-awareness through neurotransmitter visualization (84%), non-judgmental AI interaction (98%), and archetypal narrative organization (87%). The results establish that neuro-symbolic architectures are viable alternatives to large language models for digital mental health, providing the interpretability and clinical validity essential for adoption in the healthcare sector. Full article

Artificial intelligence (AI) is increasingly used in marketing to deliver personalized messages and services. Although such tools create new opportunities, their acceptance by consumers depends on several factors that go beyond technology itself. This study examines how trust and ethical perceptions, familiarity and exposure to AI, digital consumer behavior, and identity concerns shape acceptance of AI-based personalized advertising. The analysis draws on data from 650 Greek consumers, collected through a mixed-mode survey (online and paper), and tested using logistic regression models with demographic characteristics included as controls. The results show trust and ethical perceptions of acceptance as factors, while familiarity with AI tools also supports positive attitudes once trust is established. In contrast, digital consumer behavior played a smaller role, and identity-related consumption was negatively associated with acceptance, reflecting concerns about autonomy and self-expression. Demographic factors, such as age and income, also influenced responses. Overall, the findings suggest that acceptance of AI in marketing is not only a technical matter but also a psychological and social process. This study highlights the importance for firms to build trust, act responsibly, and design personalization strategies that respect consumer identity and ethical expectations. Full article

In today’s rapidly advancing healthcare landscape, integrating Artificial Intelligence (AI) and Machine Learning (ML) has the potential to significantly improve patient care and streamline medical processes. The utilization of confidential patient data to train and develop these technologies, however, raises significant concerns regarding authenticity, security, and privacy. In this study, we introduce MediChainAI, a safe and practical framework that allows patients full ownership over their own health data by integrating Self-Sovereign Identity (SSI), Blockchain, and sophisticated cryptography techniques. By clearly outlining the goals and parameters of this access, MediChainAI allows patients to safely and selectively share data with healthcare providers and researchers. While SSI guarantees that patients have ownership of their data, the framework uses Blockchain technology to keep things transparent and secure. Further, MediChainAI makes use of Merkle trees, which provide verified access to subsets of data without jeopardizing the privacy of the whole dataset. The encryption mechanism, which is based on smart contracts, is a distinctive feature of the framework that allows researchers and medical practitioners controlled and secure access to patient data. In order to improve the accuracy and reliability of medical diagnoses and treatment, this strategy makes sure that only confirmed, legitimate data is utilized to train medical models. A significant step toward safer and more personalized healthcare, MediChainAI encourages ethical and patient-focused innovation by effectively resolving essential issues regarding data security and patient privacy. Full article

With the rapid growth of Artificial Intelligence of Things (AIoT), identity management and trusted communication have become critical for system security and reliability. Continuous AI learning and large-scale device connectivity introduce challenges such as permission drift, cross-domain access, and fine-grained API calls. Traditional identity management often fails to balance privacy protection with efficiency, leading to risks of data leakage and misuse. To address these issues, this paper proposes a blockchain-based self-sovereign identity (SSI) management mechanism for AIoT. By integrating SSI with a zero-trust framework, it achieves decentralized identity storage and continuous verification, effectively preventing unauthorized access and misuse of identity data. The mechanism employs selective disclosure (SD) technology, allowing users to submit only necessary attributes, thereby ensuring user control over self-sovereign identity information and guaranteeing the privacy and integrity of undisclosed attributes. This significantly reduces verification overhead. Additionally, this paper designs a context-aware dynamic permission management that generates minimal permission sets in real time based on device requirements and environmental changes. Combined with the zero-trust principles of continuous verification and least privilege, it enhances secure interactions while maintaining flexibility. Performance experiments demonstrate that, compared with conventional approaches, the proposed zero-trust architecture-based SSI management mechanism better mitigates the risk of sensitive attribute leakage, improves identity verification efficiency under SD, and enhances the responsiveness of dynamic permission management, providing robust support for secure and efficient AIoT operations. Full article