Search Results (101)

Background/Objectives: Large language models (LLMs) and generative AI are rapidly being integrated into healthy ageing initiatives for tasks ranging from companionship and cognitive support to personalised health advice and reduction in social isolation among older adults. Current ethical discussions predominantly address bias, privacy, and accuracy, leaving unresolved three critical governance questions: How do LLM sentiments towards transformative technologies diverge from human values in ageing contexts? What epistemic status do LLM outputs hold when applied to geriatric care? When is trust in those outputs justified for older adults? And who bears responsibility when AI-informed decisions affect functional ability or well-being? Methods: The framework was developed through normative conceptual analysis, synthesizing philosophical principles of medical knowledge and trust, ethical theories of responsibility, empirical evidence on LLM sentiment divergence, digital ageism, and applications of AI in geriatric care (structured searches in PubMed, PhilPapers, and relevant databases, January 2020–March 2026). Results: The integrated framework produces (i) adaptation of SAIA for multidimensional evaluation of human–machine value convergence specific to healthy ageing values (functional ability, autonomy, dignity, equity); (ii) a four-tier classification of LLM outputs tailored to geriatric scenarios; (iii) conditions for warranted trust calibrated to age-related vulnerabilities such as cognitive decline and digital divide; and (iv) responsibility allocation via RACI models with testable hypotheses linking governance design to trust calibration and patient safety outcomes. Conclusions: Without explicit societal alignment and epistemic governance, generative AI risks reinforcing benevolent ageism, automation bias, and responsibility gaps in healthy ageing. The 2025–2027 period offers a decisive window to shape institutional norms that place functional capacity, human dignity, and value convergence at the centre of AI deployment in geriatric care. Full article

Although prior research indicates that expert reviewers identify AI-generated academic texts with low accuracy, the quantitative analysis presented in this paper has revealed marked, measurable differences between human-authored and AI-generated works. We investigate this duality in the context of Hungarian as an under-represented training language: on one hand, we perform a quantitative text analysis of the lexical, syntactic, and stylistic features of Hungarian-language academic essays by human authors (doctoral candidates) and those generated by Google Gemini, OpenAI GPT, and Anthropic Claude models. On the other hand, using a blind experimental design, we analyze how human reviewers (N = 391) with varying levels of expertise perceive and assess the quality of the texts. The quantitative analysis showed that AI-generated essays are characterized by lower lexical diversity and an absence of epistemic markers. The human evaluation yielded complex results: reviewers active in academic practice (members of the academically active and academically passive clusters) acknowledged the formal and logical precision of the AI-generated texts, yet they noted a lack of originality and critical depth. Reviewers less engaged with academic practice (members of the non-academic and inactive clusters), in contrast, were primarily persuaded by the more natural style and originality of the human-authored texts. The findings suggest that with moderate-level prompting and the provision of source literature, an AI-generated essay can be created in a few hours that reviewers deem superior to human work in certain aspects, such as formal and logical precision. Furthermore, our findings suggest that with targeted, more sophisticated prompt engineering, the quality gap between AI-generated and human-authored texts could narrow further. These findings have significant implications for assessment methods in higher education and for the regulation of academic publishing. Full article

This article develops a symbolic hermeneutic framework for interpreting contemporary socio-educational phenomena within the horizon of decolonial thought and Liberation Theology. It begins from the assumption that symbols are not merely decorative forms of representation but fundamental structures of meaning that shape both individual experience and collective life, especially through their educational effects. From this perspective, the article examines how the symbols circulating in social communication reveal the ideological underpinnings of imagination, authority, exclusion, and resistance. The essay then places this symbolic analysis in dialog with decolonial theory, arguing that the enduring epistemological legacy of colonialism continues to organize hegemonic forms of knowledge, subjectivity, and power. Particular attention is devoted to the concept of the frontier, first understood as a modern device of exclusion and then reinterpreted as a space of epistemic resistance, ethical encounter, and democratic confrontation among differences. The discussion further engages key authors of Liberation Theology and the philosophy of liberation—especially Gustavo Gutiérrez, Leonardo Boff, Enrique Dussel, and Paulo Freire—in order to show how religious discourse and pedagogical practice intersect in processes of emancipation. Methodologically, the study adopts a qualitative, interpretative approach grounded in philosophical hermeneutics and critical conceptual analysis. It reconstructs and compares major theoretical positions rather than presenting empirical data. The article argues that the integration of symbolic hermeneutics, decolonial thought, and liberationist theology offers an original framework for rethinking education as a transformative practice grounded in ethical responsibility toward the Other. By bringing the concepts of frontier, sentipensamiento, communality, and pluriverse into a single analytical constellation, the paper contributes to current debates in religious studies, critical pedagogy, and epistemic justice. In the context of contemporary global crises—migration, ecological devastation, social fragmentation, and the weakening of democratic participation—it proposes a renewed role for religion as a critical and generative force capable of opening new educational spaces for dialogue, liberation, and the reconfiguration of knowledge. Full article

Background/Objectives: Artificial intelligence reshapes clinical practice, and its effect on the clinician–patient relationship requires reconsideration of the frameworks that have shaped modern medical ethics. When clinicians delegate expertise to algorithms they cannot verify, it becomes unclear who bears clinical responsibility. Methods: This article applies a theoretically grounded normative approach to explore the ethical conditions under which artificial intelligence can be integrated into clinical practice without compromising the moral foundations of medicine. The analysis is primarily based on Pellegrino and Thomasma’s concept of the internal morality of medicine and the clinician’s act of profession. It further draws on Kantian ethics of human dignity, Levinasian relational ethics, virtue ethics, and Vallor’s concept of technomoral wisdom. Results: AI systems do not satisfy the conditions under which moral responsibility can be ascribed to them. Clinical moral agency lies in the capacity to bear three distinct responsibilities—epistemic, relational, and phronetic—none of which can be fulfilled by AI. The implementation of AI in healthcare, therefore, must occur strictly under the condition of Meaningful Human Control, rather than as a technical function of human oversight over algorithmic outputs. To ensure that MHC can function as an effective and ethically grounded safeguard, we propose five normative requirements: primacy of clinical judgement, prohibition of forced automation, traceability and explainability, transparency towards patients, and retaining clinical authority. Dialogue between clinicians and patients should remain the foundation of clinical decision-making. The proposed normative requirements aim to preserve the internal morality of medicine in a form that harmoniously combines both technological progress and established medical ethics. Full article

This paper examines 20th-century instruments that translate visual forms into sound, focusing on Daphne Oram, Iannis Xenakis, and Fernando von Reichenbach. Emerging from diverse socio-cultural contexts, these devices offer alternative art–technology configurations that challenge dominant computational paradigms. By analysing their artistic intent and technical innovation, the study positions them as speculative tools for rethinking human–machine relations and generating new technological paradigms. Extending this lineage, the author’s practice—L’UPIC Ludique—reimagines Xenakis’ UPIC as a tactile, playful instrument for children, emphasising drawing-based interaction, haptic engagement, and accessible musical creativity. Demonstrated at NIME 2024 and Rogue Open Studio 2025, the project shows how revisiting historical technologies can reconnect users with the magical, playful qualities of early drawn-sound instruments. The paper highlights the need for public investment in collaborative spaces where artistic experimentation can shape technology, reflecting Yuk Hui’s vision of art as a catalyst for epistemic transformation. Full article

Co-authored by a French humanitarian anthropologist and a South Sudanese refugee and environmental activist, both writers situated across the Global North and South, this article argues that artistic practices in displacement operate as infrastructures of survival, whose conditions of existence are both enabled and constrained by external actors. Drawing on a case study of Tongogara Refugee Settlement, it argues that the arts—and, more broadly, knowledge production—constitute key survival mechanisms across psychological, psychosocial, and identity-related dimensions. This article further shows that artistic practices in displacement are not only autonomous expressions of resilience but also mediated cultural forms whose visibility and meaning are co-produced through humanitarian, institutional, and epistemic regimes—including the regimes of academic writing itself. First, we examine art’s three interrelated survival dimensions: psychological (personal coherence amid uncertainty and symbolic mobility), psychosocial (collective bonding and mutual support), and identity (cultural representation, memory, heritage, and self-definition in displacement). Second, we examine how these functions are shaped by interactions with external actors—including humanitarian organizations, donors, cultural platforms, and academic institutions—that may increase visibility while favoring curated representation over sustained artistic development, reflecting broader donor-driven logics of accountability. Third, drawing on reflexive notes from the co-authorship process, we show how academic narration can reproduce these asymmetries, thereby positioning co-creation as both an ethical practice and an epistemic condition of equitable knowledge production. Drawing on humanitarian anthropology, aesthetics, and decolonial epistemologies, we argue that processes of symbolic and cultural reconstruction remain structurally under-institutionalized, circulating across humanitarian, developmental, and epistemic regimes without being fully claimed by any of them. Rather than offering normative prescriptions, the article traces how co-production itself becomes a site where these asymmetries are reproduced and made visible. Full article

In a post-consensus institutional landscape—where higher education systems face intensifying pressure to demonstrate strategic governance and measurable commitment to global sustainability mandates—understanding how the scholarly field of Education for Sustainable Development (ESD) has itself structurally evolved acquires both analytical urgency and policy relevance. This study maps the intellectual structure of Education for Sustainable Development (ESD) and institutional commitment in higher education through a PRISMA 2020-guided bibliometric analysis of 126 articles retrieved from Scopus for the period 2018–2025. Annual output rose from a single article in 2018 to 32 in 2025, with 46.8% of the corpus concentrated in the 2024–2025 biennium—a pattern indicative of rapid field maturation. Keyword co-occurrence analysis reveals a dual thematic architecture comprising four clusters: a Curriculum Innovation and Pedagogical Transformation axis and a strategic governance and institutional commitment axis. A notable pattern is a reorientation in the relative weight of research themes, evidenced by the growing density of terms such as governance, strategic approach, and institutional commitment in the recent literature. This governance-oriented cluster, consolidated by a core of prolific authors, shows a higher recent growth rate in co-occurrence frequency than the traditional curriculum axis. An emerging tendency toward disciplinary specialization—particularly in engineering education—and toward impact assessment is consistent with a gradual thematic consolidation of the field. The observed co-occurrence patterns are consistent with theoretical frameworks that associate scalable pedagogical innovation with institutional-level commitment and systemic governance frameworks aligned with the SDGs, although bibliometric data alone cannot establish this dependency. These patterns may signal a reorientation in the scholarly framing of ESD toward institutional design and governance questions, although confirming whether this reflects substantive epistemic change or shifts in publishing incentives requires evidence beyond bibliometric indicators. Full article

Artificial intelligence (or AI) is rapidly transforming digital learning environments, reshaping how educational processes are organized, how knowledge is produced, and how learning is evaluated. Despite a growing body of research on AI in education, existing studies often examine technological, pedagogical, and ethical dimensions in isolation, leaving a lack of integrative frameworks capable of explaining how AI restructures learning environments as a whole. This study addresses this gap by proposing a three-layer conceptual framework that models AI-mediated learning environments through the interaction of efficiency, pedagogy, and ideology. The framework conceptualizes AI integration as a system of interdependent processes: the efficiency layer captures the optimization of educational activities through automation and data-driven personalization; the pedagogical layer explains how AI reshapes learning processes, feedback cycles, and learner strategies; and the ideological layer examines the normative assumptions embedded within AI systems, including issues of epistemic authority, linguistic norms, and algorithmic bias. Drawing on a structured synthesis of recent empirical research across domains such as generative AI tools, automated feedback systems, intelligent tutoring systems, and AI-supported assessment, the study demonstrates how these dimensions interact to structure contemporary digital learning environments and generate both affordances and tensions. The main theoretical contribution lies in advancing a system-level analytical framework that moves beyond tool-specific approaches and enables a more integrated understanding of AI in education. In practical terms, the framework provides educators and policymakers with a lens to critically evaluate AI integration, supporting more informed decisions on assessment design, sustainable learning practices, and inclusive digital education. Full article

The film Black Panther (2018) has been the subject of extensive discussion, particularly within the context of representational politics in contemporary Hollywood cinema. This critical scoping review maps how academic literature interprets the film‘s treatment of colonial, racial, cultural, gender, and disability-related stereotypes and examines how these interpretations are shaped by epistemic context. A systematic search and screening process yielded 52 publications from 2018 to 2024 that were analyzed with qualitative content analysis and descriptive mapping. The review indicates that scholarly response to the film is defined by structured ambivalence. While scholarship predominantly frames Black Panther as challenging colonial and racial stereotypes, it also points to inconsistent representations and reinforced stereotyping, particularly with regard to cultural homogenization, exceptionalism, and patriarchal governance. Interpretive stances vary systematically across epistemic positions. Scholarship based in Africa emphasizes counter-stereotypical readings, whereas scholarship from the United States, accounting for half of the reviewed contributions, displays greater interpretive diversity, including more critical and ambivalent positions. These findings suggest that Black Panther does not function as a counter-stereotypical text. Rather, it is a site where representational politics in global blockbuster cinema, industry constraints, and epistemic authority intersect, extending the soft-power dynamics of the Marvel Cinematic Universe into academic knowledge production. Full article

Artificial intelligence (AI) systems increasingly mediate how individuals learn, work and make decisions, raising foundational philosophical questions about the nature of knowledge, agency and autonomy. This article integrates philosophical analysis with illustrative empirical cases from Romania to examine how AI restructures human epistemic and practical activity. A central empirical observation, the engagement–performance paradox, reveals that AI-driven learning environments can produce dramatic increases in learner interaction while generating only marginal improvements in understanding. Interpreted through post-phenomenology, virtue epistemology and theories of autonomy, this paradox highlights the emergence of epistemic superficiality: a condition in which algorithmically mediated engagement replaces reflective, conceptually grounded learning. Complementary findings from AI-supported workplace contexts further illustrate how intelligent systems automate aspects of decision-making, thereby reshaping autonomy, responsibility and the phenomenology of action. Synthesizing these insights, the article argues that AI functions as a structuring force that co-authors human agency by reorganizing the conditions under which cognition and action occur. The study contributes to contemporary debates in the philosophy of technology, epistemology and AI ethics by proposing the concept of structured agency as a lens for understanding how AI-mediated environments transform the foundations of knowledge, autonomy and human flourishing. Full article

This article examines how LGBTQ+ allyship is made, felt, and negotiated within a secondary school workshop using creative, participatory methods. Drawing on affect theory (see Sara Ahmed) and feminist new materialist scholarship (see Barad, Renold, among others), the paper analyses a collaborative collage activity centered on Pride flags and symbolic materials. Rather than treating allyship as a fixed identity or a knowledge-based achievement, the study explores how it emerges relationally through encounters with materials, symbols, bodies, and digital technologies. Through close analysis of moments of uncertainty, affective attachment, cutting and shattering of symbols, and the collective naming of the final artwork, the article traces how not-knowing, pleasure, confusion, and togetherness function as generative forces for allyship. The workshop is framed as a propositional research-creation space in which phones, Google searches, bunting, scissors, and book references intra-act with young peoples’ lived experiences, redistributing epistemic authority and unsettling school-based expectations of correct knowledge. The findings contribute to existing research on LGBTQ+ inclusion and allyship in schools by shifting focus from identity labels and institutional frameworks toward the affective, material, and speculative processes through which allyship is assembled in the moment. In doing so, the paper offers an alternative conceptualisation of allyship as relational practice rather than static position, with implications for creative pedagogy and inclusive educational research. Full article

This study investigates how teacher-education undergraduates in Greece use, evaluate, and trust Artificial Intelligence (AI) in higher education, with particular attention to the gap between widespread adoption and limited epistemic trust. The topic is important because generative AI is rapidly entering universities, reshaping learning practices, academic integrity, and the legitimacy of knowledge, while learners often rely on systems whose outputs are not easily verifiable. The study focuses on future teachers because they are both current users of AI in higher education and likely future mediators of its use in school settings. Addressing this problem, the study contributes empirical evidence on how AI adoption relates to epistemic authority and institutional legitimacy within teacher education rather than across university students in general. A mixed-methods design was employed using a structured questionnaire completed by 363 teacher-education undergraduates from the University of Patras and the University of Ioannina in Greece; the sample was predominantly women (86.0%) and first-year students (92.6%). Quantitative responses were analyzed statistically, open-ended answers were examined thematically, and factor analysis was used to identify latent attitudinal dimensions. The findings indicate very high AI use in everyday life (92.6%) and study practices (81.3%), but only moderate trust: 1.4% reported complete trust and 12.1% generally trusted AI-generated answers. Six dimensions explained 61.73% of total variance, pointing to a layered attitudinal structure within this teacher-education population, consistent with an adoption–trust paradox and with the need for transparent, verifiable, human-supervised educational AI. The observed verification-based trust calibration may partly reflect an emerging pedagogical orientation toward source checking and responsibility for knowledge mediation, but given the strong concentration of first-year students, this should be interpreted as characteristic of early-stage teacher education rather than of university students more broadly. Full article

This paper argues that the most significant challenge artificial intelligence poses to theological anthropology is not ontological but epistemic. Rather than asking whether machines can think, feel, or bear the image of God, this paper redirects attention to the prior question of what happens to the human when core epistemic capacities, judgment, discernment, interpretive authority, and moral reasoning are progressively delegated to computational systems. Drawing on the concept of epistemic automation, understood as the systematic transfer of knowledge-producing functions from human agents to algorithmic processes, this paper develops a threefold analytical framework. First, it distinguishes epistemic authority from ontological status as the more productive locus for theological anthropological inquiry. Second, it introduces the distinction between fluency and understanding as an anthropological boundary condition that AI renders newly visible. Third, it analyses delegated cognition as a form of agency deformation with theological significance. The paper concludes that theological anthropology must move beyond reactive commentary on AI and instead generate a theory of the human under conditions of epistemic transformation. The argument engages constructively with philosophy of technology, social epistemology, and Christian theological traditions to offer a framework applicable across confessional boundaries. Full article

Industrial development is commonly described as a sequence of technological stages, from automation to artificial intelligence. This study examines whether successive industrial paradigms—from Industry 3.0 to the emerging Industry 6.0—can be more adequately understood as transformations in technological rationality rather than merely technological upgrades. The analysis adopts a conceptual–philosophical methodology informed by targeted review of peer-reviewed literature indexed in Scopus and Web of Science, integrating Kuhn’s notion of paradigms with Peircean inferential logic. Through systematic comparison of technological configurations, problem-framing practices, and epistemic assumptions, the study maps each paradigm onto a dominant mode of inference. The findings indicate that Industry 3.0 privileges deductive rule-based control, Industry 4.0 relies on inductive data-driven optimization, Industry 5.0 foregrounds hermeneutic interpretation and normative judgment, and prospective Industry 6.0 can be coherently interpreted as oriented toward abductive hypothesis generation within human–AI systems. Industrial change thus emerges as a reconfiguration of epistemic limits rather than a linear trajectory of technical improvement. The analysis concludes that expanding machine intelligence does not eliminate human authority but intensifies epistemic responsibility, understood as the obligation to determine relevance, value, and legitimacy in socio-technical systems. Full article

Background/Objectives: Large language models (LLMs) such as ChatGPT are rapidly being integrated into healthcare for tasks ranging from clinical documentation to diagnostic support. Current ethical discussions focus predominantly on bias, privacy, and accuracy, leaving three critical governance questions unresolved: What kind of knowledge does an LLM output represent in clinical reasoning? When is a clinician’s or patient’s trust in that output justified? Who bears responsibility when an AI-informed decision leads to patient harm? This study proposes the Epistemic Authority–Trust–Responsibility (ETR) Architecture, a normative conceptual framework that addresses these three questions as an integrated governance challenge. Methods: The framework was developed through normative conceptual analysis—a method that constructs governance proposals by synthesising philosophical principles, ethical theories, and empirical evidence. The literature was identified through structured searches of PubMed, PhilPapers, and EUR-Lex (January 2020–March 2026), drawing on the philosophy of medical knowledge, the ethics of trust and testimony, and the moral philosophy of responsibility. Results: The ETR Architecture produces four outputs: (i) a four-tier classification system that distinguishes LLM outputs—from administrative drafts to clinical evidence claims—and matches each tier to appropriate verification requirements; (ii) the concept of the ‘epistemic placebo’, formally defined as a governance measure that creates a documented appearance of compliance while lacking at least one operative element of genuine oversight; (iii) a model specifying four conditions under which trust in healthcare AI is justified; (iv) four testable hypotheses with associated research designs connecting governance design to trust calibration and patient safety. Conclusions: The 2025–2027 regulatory transition period offers a critical window for shaping how healthcare institutions govern AI. We argue that deploying LLMs without explicitly classifying their outputs and building appropriate oversight risks allows governance norms to be set by technology vendors rather than by evidence-informed, patient-centred policy. Full article