Search Results (129)

Artificial intelligence (AI) is increasingly integrated into healthcare education, clinical decision-making, and future practice. For medical students, AI literacy entails technical understanding, practical competence, ethical awareness, value-based judgment, and responsible engagement. This study examines how culturally embedded value orientations are associated with Chinese medical students’ perceived AI literacy, as assessed using a self-report instrument. In a cross-sectional sample of 1500 medical students enrolled at a comprehensive university in Henan Province, China, AI literacy was assessed using the 12-item Artificial Intelligence Literacy Scale (AILS), a self-report measure whose scores represented perceived AI literacy. Value orientations were measured using the 32-item Chinese Values Questionnaire (CVQ), comprising eight value dimensions. NCA and fsQCA were conducted to examine necessary conditions and configurational associations with membership in the high perceived AI literacy set. No single value dimension met the criterion for set-theoretic necessity with respect to membership in the high self-reported AI literacy set, and no individual condition met the fsQCA necessity consistency threshold of 0.90. Four sufficient configurations associated with high perceived AI literacy were identified, with an overall solution consistency of 0.867 and coverage of 0.383. Moral Self-Discipline and Public Interest repeatedly appeared as core or peripheral conditions. These results suggest that high perceived AI literacy was associated with multiple combinations of value orientations rather than with a single value dimension. High perceived AI literacy was associated with multiple value configurations rather than one dominant value orientation. Empirically, this study applies configurational analysis to understand how value orientations are associated with AI literacy, complementing existing research on knowledge, attitudes, and readiness. These context-bound associations may inform future research on whether medical AI curricula can integrate technical training with ethical reflection and public-oriented professional values. Full article

Background: Artificial intelligence (AI) systems, including large language models, are increasingly used in clinical practice, whether consulted informally by clinicians or introduced by employers into decision workflows. It remains unclear how physicians attribute moral responsibility when a decision follows an AI recommendation and whether that attribution varies with the type of decision at stake. Methods: We conducted a cross-sectional, within-subject vignette survey of physicians in Romania. Each respondent rated the same three scenarios—urgent clinical, elective clinical, and administrative—in which a physician followed an AI recommendation under two extenuating institutional constraints. Five-point Likert items addressed the mitigation of blame by circumstances, physician responsibility despite the AI recommendation, and institutional co-responsibility. Analyses were non-parametric, with corrections for multiple testing. Results: Among 72 physicians from 17 specialties, respondents endorsed full personal responsibility in every scenario, including the administrative one, with no significant difference between scenarios. They rejected extenuating circumstances as mitigating in both clinical scenarios but were divided about them in the administrative scenario, which had the largest effect. Institutional co-responsibility was endorsed alongside personal responsibility rather than in place of it, and the two attributions were largely uncorrelated. No demographic association survived correction, although the study was not powered to detect small-effect sizes. Conclusions: Physicians treated AI as an instrument rather than a bearer of responsibility, which is unsurprising. The substantive findings lie elsewhere: personal responsibility was retained across all decision contexts, while what varied was the admissibility of institutional constraints as excuses and the emphasis placed on the institution’s share. Because respondents did not treat responsibility as a fixed quantity to be divided, the pattern is consistent with distributed-responsibility accounts rather than with a responsibility gap, though attitudinal data cannot adjudicate between normative accounts. The findings are exploratory and require confirmation in larger, more representative samples. Full article

Generative artificial intelligence (AI) is entering religious communication not only through chatbots and generated text, but also through images and reels on social media. Yet research in this field has paid limited attention to what images contribute beyond their accompanying words. This article examines how AI-assisted visuals are incorporated into Indonesian Muslim Instagram accounts linked to Nahdlatul Ulama (NU) and what communicative work they perform. Informed by the Religious Social Shaping of Technology framework and visual-culture scholarship, the study combines qualitative digital observation with content and multimodal analysis of eleven posts from three accounts and sampled comments. The visuals were adopted selectively within established organisational, humour-oriented, and preacher-led modes of address. AI functioned through amplification: synthetic construction gave bodily and spatial form to themes of national belonging, moral criticism, santri conduct, and interreligious engagement, while enabling encounters that could not readily be photographed. These moral positions became legible through familiar signs, but authority remained with the organisation, collective voice, or public persona through which the images were framed and circulated. AI-assisted imagery is therefore best understood as situated visual mediation: it expands what religious communicators can picture without independently determining the meaning or authority of what is pictured. Full article

Generative AI (GenAI) adoption in higher education (HE) raises significant ethical concerns. The focus is shifting from rules- or outcomes-based learning environments towards the development of moral character, personality traits, integrity, and practical wisdom (phronesis). However, most existing AI ethics validation instruments are predominantly shaped by Global North paradigms. In Global South HE contexts, in particular, open distance e-learning (ODEL) HE institutions (HEIs) characterised by limited direct supervision and a digital divide, validation remains scant. Ethical risks are intensified by the adoption and integration of GenAI tools, such as large language models (LLMs), to enhance teaching, learning, research, and student support, thus recognising the need to develop and validate virtue ethics scales. The current paper attempts to address this gap by validating the Virtue Ethics Measurement Scale (VEMS) within South Africa’s largest comprehensive ODEL institution. Guided by the positivist paradigm, a 36-item cross-sectional survey of 503 undergraduate and postgraduate students measured six virtue dimensions (justice, honesty, responsibility, care, prudence, and fortitude). Confirmatory factor analysis (CFA) compared four competing models. The single-factor model showed poor fit, rejecting unidimensionality. A second-order hierarchical model demonstrated an acceptable fit (χ²/df = 2.992, CFI = 0.933, RMSEA (Root Mean Square Error of Approximation) = 0.063, SRMR (Standardized Root Mean Squared Residual) = 0.043) with subscale reliabilities ranging from Cronbach’s α = 0.84 to 0.90, supporting a multidimensional yet hierarchical virtue structure. The VEMS offers a psychometrically sound instrument for evaluating ethical AI use in ODEL institutions. This aligns with virtue ethics theory, which emphasises that moral character is a constellation of dispositions (e.g., honesty, care, prudence) rather than a single trait. The VEMS thus enables HEIs to assess students’ virtues, design targeted ethics capacity-development programmes, and inform policy reform for responsible GenAI adoption in under-researched Global South HE settings. Full article

Artificial intelligence (AI) increasingly mediates leadership-relevant judgment through models, dashboards, metrics, decision-support systems, and autonomous agents. This conceptual article develops a socio-technical systems theory of systemically mediated leadership, defined as a nested system-level condition and recurrent process configuration through which human actors, AI systems, organizational routines, governance institutions, and affected stakeholders jointly produce and revise direction, meaning, consequential judgment, legitimacy, and accountability through recursive feedback. A problem-driven conceptual synthesis was updated through 3 August 2026. A structured discovery pass yielded 97 candidate records; 85 sources were retained after relevance screening, citation chaining, concept mapping, and comparison of eight candidate mechanism families. Four proposed qualification conditions jointly define the construct within the present framework: AI mediation, leadership relevance, distributed judgment, and recurrent institutional embedding. Five mechanism families explain transformations in responsibility, legitimacy, control, attention, and feedback timing: moral delegation, interpretive laundering, ceremonial oversight, metric-driven sensemaking, and ethical latency. A causal-loop model specifies justificatory reinforcement, capability atrophy, power insulation, and accountable correction. Their relative dominance produces three ideal-type dynamic regimes: accountable adaptation, stabilized trade-offs, and destructive drift. The theory predicts that organizations using equally accurate models may produce divergent leadership and accountability outcomes because their feedback, power, and oversight architectures differ. Responsible AI leadership thus depends on system architecture and contestable institutional practice, not leader intention, formal human approval, or model accuracy alone. Full article

Against the backdrop of the rapid popularization of AI companion technologies and intimate interaction technologies in contemporary China, this article challenges the traditional normative ethical framework centered on emotional authenticity, deception, and moral legitimacy. It argues that early debates over AI intimacy overemphasized the authenticity of machine emotions and the risks of deception while ignoring the lived relational practices of users. By introducing queer phenomenology (Sara Ahmed) as an analytical tool, the paper reinterprets AI-mediated intimacy through the core concepts of orientation, lines, and sticky emotions, viewing emotions as relational effects rather than as internal psychological states and AI companions as constitutive participants in shaping relational orientations. Empirical phenomena such as AI romantic partners, Doubao conversational intimacy, and cyber widowhood demonstrate that AI intimacy has become a normalized affective infrastructure in Chinese daily life, whose value lies not in pre-set ethical judgments but in its dynamic reconfiguration of human subjectivity, emotion, and relationality. This study provides a new non-normative, practice-oriented theoretical framework for understanding digital intimacy in the algorithmic age. Full article

This article examines the development of Catholic reflection on media, communication, and technology from nineteenth-century debates over press freedom to contemporary interventions on artificial intelligence. Drawing on a purposively selected corpus of Catholic documents issued at the universal level between 1832 and 2026, it combines historical–hermeneutical reconstruction, comparative document analysis, and juridical–institutional interpretation. The study distinguishes three heuristic phases: defensive regulation and the construction of Catholic media institutions; pastoral appropriation, dialogue, media education, and digital adaptation; and, since 2020, an anthropological deepening accompanied by growing concern with the political, economic, juridical, and social structures of AI. Particular attention is given to the Rome Call for AI Ethics, the two papal messages of 2024, Antiqua et nova, and Magnifica humanitas. The article argues that this history does not reveal an unchanged doctrine of human dignity, but a developing anthropological concern whose vocabulary, theological grounding, and normative function change over time. Earlier concerns with truth, moral order, ecclesial responsibility, and the salvation of souls are progressively reformulated through communication as communion, participation, rights, human agency, and integral human development. Full article

Can Hans Jonas’s heuristics of fear still ground a philosophy of artificial intelligence (AI), or does the digital artefact require its reformulation? Building on the empirical turn in the philosophy of technology and on a postphenomenological reading of human–technology relations, this article combines conceptual reconstruction with a PRISMA 2020-compliant bibliometric review (Scopus + Web of Science, 2010–2026; n = 2372 articles and reviews). The bibliometric map reveals an extreme productivity concentration, a stable journal core, and six thematic clusters that organise the field. Rather than treating these patterns as proof of a normative thesis, the article uses them to locate a lexical re-weighting of the field after 2021, in which applied AI-ethics and governance vocabularies expand faster than explicitly philosophical vocabularies. A targeted Jonasian sub-corpus (n = 124) indicates that Jonas remains conceptually relevant but circulates mainly in a smaller, database-visible niche of continental and environmental ethics. We argue that the heuristics of fear remains philosophically fertile provided that it is reconstructed as a threshold question of moral admissibility and expanded through phenomenological resources—lifeworld, embodiment, technological mediation—and through the cosmotechnical plurality emphasised by Yuk Hui. The convergence of bibliometric mapping and philosophical analysis suggests that robust AI governance requires not only procedural principles but also renewed attention to digital mediation, distributed agency, and the protection of the lifeworld under algorithmic uncertainty. Full article

Text-to-image generation is an increasingly fast-paced field of generative artificial intelligence, consisting of synthesizing images of high quality and semantic consistency based on natural language descriptions. In this paper, we give an extensive overview of the approach to text-to-image generation using deep learning, including the most common core model families, architecture designs, training approaches, and evaluation systems. We discuss the paradigms of the generative adversarial networks (GANs), variational autoencoders (VAEs), transformer-based designs, and diffusion models, with the last one representing the state of the art in image generation models. The review also discusses key aspects of pipelines such as text encoding, cross-modal alignment, mechanisms of attention, and decoding images. Popular datasets, methods, and metrics of evaluation, including Fréchet Inception Distance (FID) and CLIP-based similarity, are discussed. The application domains that involve creative content creation, medical imaging, education and industrial design are critically discussed. Despite significant advances, various issues still exist, such as low stability in training, excessive computational complexity, amplification of bias, generated images, and text–image alignment errors. Moral and social issues, such as misinformation, intellectual property, and equity, are critically examined. Lastly, we present future research directions to more controllable, more efficient and more interpretable text-to-image systems, focusing on multimodal foundation models and human–AI collaborative design. Full article

Artificial intelligence (AI) governance has converged around transparency, accountability, safety, privacy, fairness, human rights, human oversight and risk management. However, this vocabulary remains limited when AI is understood as a sociotechnical force shaping agency, work, truth, democratic trust, meaning and long-term human futures. This article is a critical conceptual review, with a structured Scopus-based mapping of 48 peer-reviewed articles, a critical documentary analysis of six international AI governance frameworks and a supplementary interpretive engagement with Magnifica Humanitas. Using a PRISMA-informed flow diagram for transparent reporting and abductive thematic synthesis, the study identifies four fragmented literatures: AI ethics and governance; anthropological accounts of dignity, autonomy, agency and vulnerability; spiritual and theological approaches to meaning and moral formation; and civilizational analyses of democracy, war, transhumanism and existential risk. The findings do not suggest that these concerns are absent from AI ethics; rather, they show that they remain dispersed and are not consistently translated into governance criteria. This article proposes the Anthropological, Spiritual and Civilizational (ASC) Framework as a diagnostic heuristic for extending trustworthy AI toward dignity, truth, social justice and humane futures. The framework is conceptual and requires future empirical and expert validation. Full article

Artificial intelligence (AI) is widely viewed as a technological foundation for organizational resilience. Yet firms may strategically exaggerate their AI-related narratives without corresponding substantive investment. This study examines whether such AI washing is associated with lower organizational resilience. We conceptualize AI washing as a narrative–investment misalignment within organizational systems, in which symbolic AI claims move ahead of substantive AI investment and capability formation. Based on Chinese A-share listed firms during 2010–2024, we develop a firm-level AI washing index by comparing firms’ within-industry ranking in AI disclosure with their within-industry ranking in actual AI investment. AI disclosure is identified from annual reports using a large language model, while actual AI investment is measured through AI-related software and hardware investments. Using double-debiased machine learning, we estimate a significantly negative association between AI washing and organizational resilience. Economically, a one-standard-deviation increase in AI washing is associated with a decline in organizational resilience equivalent to approximately 3.276% of the average annual change in organizational resilience. This estimated pattern remains stable when we employ alternative variable constructions, replace the machine learning algorithms, adjust the cross-fitting folds, use propensity score matching, and further apply a deep instrumental variable strategy. Mechanism tests based on organizational legitimacy provide evidence consistent with legitimacy-related transmission channels, suggesting that AI washing is associated with lower resilience through weakened pragmatic, moral, and cognitive legitimacy under the maintained mediation assumptions. Further analysis reveals an asymmetric pattern: firms whose AI narratives exceed actual investment experience lower resilience, whereas firms whose actual investment exceeds external narratives exhibit higher resilience. The negative estimated association is particularly evident in high-tech industries, enterprises with established bank-firm ties, and enterprises with higher educational heterogeneity in their top management teams. This study advances research on AI disclosure and organizational resilience by showing that symbolic AI narratives can signal system-level fragility when technological claims are misaligned with substantive capability formation. Full article

The development of artificial intelligence has proceeded within a disciplinary culture whose positivist epistemological foundations generate a constitutive blind spot: the systematic exclusion of tacit, embodied, relational and contextually situated knowledge from what counts as knowledge at all. This article argues that this exclusion is not a technical limitation, but a structural condition of AI systems as currently built, with serious consequences for individuals, institutions and the social order. The method is interdisciplinary and critically synthetic, integrating the anthropology of science and technology, the philosophy of language and the literary-philosophical tradition of techno-critique. Three original contributions are advanced. First, the Faustian framework is reformulated as a collective rather than individual pact: the AI contract implicates a civilisation. Second, the prevalent reductionist critique is refined: the error is not quantification of incommensurable goods but the enforcement of total orderings upon value landscapes that admit only partial orderings. Third, the stochastic parrot objection is reconciled with the Faustian analysis through the concept of institutional amplification: AI’s danger resides not in its intelligence but in the authority conferred upon its incomprehension. The article concludes that Mephistopheles, not Faustus, is the more precise figure for AI itself, and asks whether the defunding of humanities disciplines, those best placed to navigate the moral challenges AI presents, constitutes the most consequential characteristic deletion of all. Full article

Background and Objectives: Hospitals increasingly use AI tools that give nurses on-screen alerts and recommendations (AI-supported clinical decision support, AI-DSS). When nurses override these alerts too often, useful guidance can be lost; when they trust them blindly, errors can slip through. We examined which work and wellbeing factors are associated with nurses’ self-reported intention to override AI alerts, rather than observed override behavior. Methods: We surveyed 239 registered nurses (76.6% female; mean age 33.7 years) at a large hospital in Timișoara, Romania, from January to March 2025. Questionnaires measured alert fatigue, moral distress, mental workload, sleep problems, resilience, team psychological safety, and how strongly nurses intended to override AI alerts. Results: Nurses fell into three groups: those who tended to over-trust AI (26.8%), those with balanced trust (41.0%), and those who resisted it (32.2%). The resistant group had the strongest intention to override alerts and the weakest sense of psychological safety. Alert fatigue was the factor most strongly associated with override intention, and this association was partly accounted for by moral distress. The indirect association was weaker among nurses reporting higher team psychological safety. An exploratory model using these factors distinguished nurses with high self-reported override intention with acceptable accuracy. Because all variables were measured at a single time point, findings are associative and hypothesis-generating rather than causal. Conclusions: How nurses respond to AI alerts depends less on the technology than on their workload, ethical strain, and team climate. Cutting unnecessary alerts, easing moral distress, and building psychological safety may help nurses use AI more safely. Full article

This study conducts a technical analysis of frontier generative AI algorithms—including Meta’s Self-Rewarding Language Models, DeepMind’s EVA (Evolving Alignment via Asymmetric Self-Play) framework, and DeepSeek’s pure reinforcement-learning models—in order to examine an intrinsic paradigm shift in the ethical governance of generative artificial intelligence and to advance a physicalist analysis of algorithmic endogenous ethics. Combining a close reading of alignment techniques (RLHF, DPO, iterative DPO, GRPO) with a conceptual analysis grounded in Peter-Paul Verbeek’s theory of technological mediation and moral materialization, the paper traces how value-alignment goals are being “materialized” into internal, dynamic, and evolvable “moral scripts” within the algorithms themselves. The analysis shows that contemporary alignment practices are moving from external ethical discipline toward endogenous norms generated through iterative self-evaluation, asymmetric self-play, and rule-based self-exploration. The paper argues that this trend warrants a re-examination of Verbeek’s framework for its capacity to explain the co-evolution of technology and morality in the digital age, and it envisions a future of human–machine value co-evolution organized around new research directions such as “Setting as Governance” and “value homeostasis mechanisms”. Full article

This article addresses the theological implications of Artificial Intelligence (AI) within the broader Catholic theology of technology. The emergence of AI challenges the traditional theological accounts of technicity and human action and demands a renewed reflection capable of engaging current technological developments and transformations. The study first examines artificial intelligence through the point of view of postphenomenology, highlighting the mediating role of technology in human perception, agency, and world-formation. It then revisits the theological concept of the imago Dei, central to Christian reflections on human activity and technology, by placing in dialogue different theological interpretations of the image of God and their anthropological implications. Offering a brief bibliographic and interdisciplinary review, the article analyses how current debates on AI and the imago Dei reshape questions concerning human uniqueness, creativity, embodiment, and moral responsibility. A renewed theological interpretation of the image of God, integrated with contemporary philosophies of technology, offers valuable insights into the nature of human action and human nature within technologically mediated contexts. The article concludes that Catholic theology can contribute a distinctive and critically constructive perspective to current discussions on artificial intelligence by articulating a more dynamic and relational understanding of humanity, human activity and technological mediation. Full article