Search Results (1,098)

As generative AI expands the technical frontiers of prediction, measurement, and design, a growing tension has emerged between algorithmic fluency and institutional trust. This conceptual article offers a narrative synthesis of recent work in learning analytics, educational data science, human–AI interaction, and AI governance to propose stewardship as a necessary fourth paradigm of educational data science. Stewardship represents the professional, epistemic, and institutional work of governing judgment in an environment where analytic systems are increasingly generative and persuasive. Rather than treating stewardship as a general ethics checklist, the article positions it as the governance of epistemic and pedagogical authority: who determines what counts as evidence, interpretation, and educational action when AI systems help produce those judgments. The synthesis suggests that while GenAI can support bounded analytic tasks, evidence for systemic educational transformation remains limited and uneven. The field’s primary challenge is therefore not technical performance alone, but the governance of interpretation, validation, delegation, and action. By centering provenance, uncertainty, accountable oversight, learner agency, and institutional learning, stewardship provides an actionable framework for anchoring analytic innovation in responsible educational improvement. Full article

This contribution examines the role of artificial intelligence technologies in the co-construction of social reality, with specific attention to AI-generated data as emergent agents of knowledge production. Building on perspectives from science and technology studies and recent debates on algomorphic sociology, the contribution conceptualizes generative AI systems not as research instruments, but as active participants in epistemic processes. The analysis argues that AI-generated data exhibit a performative character: they do not simply represent social phenomena but actively contribute to their stabilization, classification, and circulation. This performativity fosters a shift from researcher-centered interpretation toward hybrid configurations in which meaning emerges through human–machine assemblages. Through a theoretical synthesis of recent methodological and epistemological reflections, the contribution highlights a transition from anthropocentric models of knowledge production to post-anthropocentric, relational frameworks in which agency, cognition, and sense-making are distributed across sociotechnical networks. The contribution concludes by outlining the implications of this shift for the future of digital social research and also for reflexivity, methodological design, and the ethics of social research, advocating a critical and adaptive stance toward AI as a co-producer of knowledge rather than a subordinate analytical tool. Full article

This study investigates the vanished Fazlı Pasha Palace in Istanbul as a case of lost architectural heritage, addressing the challenges of heritage interpretation, presentation, and integration into contemporary urban contexts. Drawing on contemporary conservation frameworks, the research situates the palace within a broader discourse on cultural and urban sustainability, emphasising the interdependence of tangible and intangible heritage values. As a methodology, this study employs a multi-layered, interdisciplinary framework that synthesises archival empirical data, architectural historiography, and GIS-based geospatial analytics. Unlike traditional descriptive methods, this research introduces an integrated digital heritage interpretation model grounded in an evidence-grading system. This system categorises architectural data into three distinct epistemic levels: documented (empirical), inferred (analogous), and hypothetical (conjectural). By implementing this tripartite structure, the design ensures a structured communication of uncertainty, effectively bridging the gap between historical fragmentation and spatial data and stratification while strictly adhering to contemporary conservation approaches that critically limit speculative reconstruction in the cases of lost urban layers. The findings, supported by GIS spatial mapping, demonstrate how the palace’s administrative footprint influenced 18th-century Ottoman Istanbul’s urban fabric, of which there is very limited spatial knowledge. Moreover, proposals for effectively reintegrating lost architectural heritage into contemporary urban memory without compromising authenticity or the integrity of existing urban fabric are developed. In doing so, the study contributes to urban sustainability by offering a non-intrusive, reversible, and critically evidence-based approach to heritage interpretation. Beyond the specific case of the Fazlı Pasha Palace, the proposed model provides a transferable methodological framework for the interpretation of lost heritage in complex historic cities, supporting the continuity of cultural memory, identity, and place-based narratives. The research thus advances current debates on digital in-situ presentation of lost heritage, authenticity, and sustainable urban conservation by demonstrating how the memory of vanished buildings can be meaningfully presented and communicated within contemporary urban environments. Full article

The concept of technological singularity is discussed here in the context of architecture (of buildings, not software). This is the point at which non-human intelligence is conjectured to surpass ordinary human cognitive limits. Empirically constrained AI may already offer a useful corrective to mainstream architectural culture in one crucial aspect—its capacity to evaluate design that adapts to human emotional health. Postwar building architecture as an institutional power system rewards abstraction and stylistic conformity through media prestige while not always accounting for embodied human experience. By narrowing judgment criteria, architectural studio pedagogy trains tacitly for imitation, not seeking evidence that conflicts with dominant formal ideologies. Yet findings from environmental psychology, health-related design research, neuroscience, and recent AI-based studies show that built form measurably affects empathic response and user well-being. This paper examines what effects dominant architectural culture could impose on the public by producing informationally impoverished, stressful environments. We argue that built environment design may suffer from an epistemic closure because (i) architectural education does not foster curiosity in how design affects users—the core mechanism for intelligence development—and (ii) architectural media may legitimate non-adaptive form languages by habituating populations to ignore distress signals from geometries associated with elevated stress markers. However, empirically constrained AI can now be directed to apply that relevant knowledge base to improve the built environment. The most suggestive evidence in the paper is that LLM emotional scores, LLM geometric scores, human eye-tracking, and large public surveys converge on the same designs. In this sense, the AI singularity can be framed as a domain-specific, testable hypothesis in architecture. This paper does not report new generated results derived from Empirically Constrained Scaffolding (ECS), which appear in prior applications, but reproduces the original prompts as an illustration of the method. Full article

The Liezi constitutes an important link between the Lao-Zhuang tradition and later Daoism, yet it has long remained marginal in scholarship due to controversies surrounding its provenance. Moving beyond debates over textual authenticity, this article examines Liezi’s reflections on life and death through a comparative dialogue with Plato’s account of the soul’s journey with you 游 as a bridging notion. In the Liezi, life is construed as a temporary wandering and death an inevitable return. This understanding weakens fixed subjectivity and normative structures, articulating a mode of cultivation that emphasizes accommodation, detachment, and the coexistence with the myriad things in a quasi-religious cosmos. In Plato, by contrast, the soul persists as a moral subject journeying between the sensible and intelligible worlds. It is integrated into a religious cosmic order that unites epistemic with ethical dimensions and is ultimately oriented toward its purification and return to the intelligible world. The comparison reveals a structural divergence between the two thinkers in the ultimate placement of human existence, while also demonstrating how cross-cultural comparison can generate mutually illuminating insights and reopen reflection on the Liezi’s place in intellectual history. Full article

This study examines the phenomenon of pseudoconfident knowledge in the context of the everyday use of generative artificial intelligence. By pseudoconfident knowledge, we mean a response that is substantively plausible, rhetorically coherent, and outwardly persuasive but is treated and understood as knowledge before its actual reliability has been established. Of course, we do not use the term “pseudoconfident knowledge” to denote knowledge in the strict epistemological sense. Rather, it denotes a special form of AI-generated content that acquires the status of knowledge in the user’s perception before its reliability, source-based justification, or factual correctness have been established. The problem here is not that such an answer is already knowledge but that it is prematurely accepted as knowledge because of its coherence, completeness, and rhetorical confidence. The aim of the study is to identify the epistemic gap between the everyday operational integration of artificial intelligence and the user’s critical ability to distinguish between persuasiveness and justification. The theoretical framework combines approaches to AI literacy, epistemic vigilance, and contemporary forms of digital mediation in the circulation of knowledge. The empirical basis of the study is an online survey of AI users. The analysis was conducted using descriptive statistics, contingency tables, and methods for testing associations between categorical variables. The results show that the key differentiating factor is not the frequency of AI use, but the strategy used in handling its responses. More epistemically robust positions are associated with practices of comparison, editing, and verification, whereas uncritical acceptance of the answer is associated with greater vulnerability to pseudoconfident knowledge. We conclude that the spread of generative artificial intelligence is producing a new socioepistemic problem that calls for a shift in emphasis from simple instrumental literacy toward a culture of verification, doubt, and epistemic responsibility. Full article

Venture novelty enables startups to overcome entry barriers and establish differentiated competitive advantages. However, research examining its antecedents from an epistemic control perspective remains limited. Drawing on survey data from 230 entrepreneurs and employing structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA), this study investigates how entrepreneurs’ critical thinking influences venture novelty. The findings reveal a dual effect. On the one hand, critical thinking promotes venture novelty by fostering interactive learning, which facilitates the integration of heterogeneous information and the refinement of entrepreneurial opportunity insights. On the other hand, critical thinking increases cognitive depletion, thereby constraining the cognitive resources available for innovative activities. Furthermore, imagination moderates these relationships by strengthening the positive effect of interactive learning while attenuating the negative impact of cognitive depletion. FsQCA results further identify four configurational pathways to high venture novelty. This study contributes to the literature by stating both the enabling and constraining mechanisms of entrepreneurs’ critical thinking, clarifying its dual role in epistemic control, and providing configurational evidence regarding the role of imagination in fostering entrepreneurial innovation. Full article

This article critically examines the use of generative AI (specifically ChatGPT-4) as a tool for designing teaching materials in university courses on quantitative social research methods. It is conceived as a concept paper grounded in an illustrative, AI-assisted co-design session. The purpose is not to evaluate learning outcomes or produce generalizable empirical findings, but to develop a theory-informed analytical framework for examining AI-generated materials as epistemic artifacts. The analysis illustrates how seemingly neutral AI outputs embed specific assumptions and can actively shape the way social research is approached, intensifying constitutive methodological conventions. By critically unpacking the simulated outputs, the article proposes a framework for integrating AI-generated content into quantitative methods education as an object of critical inquiry. Full article

Cariño (care) should be central to equity-centered transformation in science, technology, engineering, and mathematics (STEM) higher education. Yet, relational leadership practices that prioritize culturally grounded care—such as cariño—are often absent in STEM initiatives, leaving unexamined how Women of Color (WOC) enact these practices to advance equity for historically marginalized students. Employing a qualitative methodology grounded in Chicana Feminist Epistemology, in-depth interviews were conducted with five WOC leading a multi-institutional, federally funded STEM initiative. Analysis revealed four interrelated dimensions of what we are calling “Cariño Competence”: (1) relational attunement grounded in moral obligation, (2) protective action when project systems fail students, (3) boundary-setting as care and resistance to extractive labor, and (4) community-sustained resilience through networks of WOC leaders. The findings offer a data-driven theorization of Cariño Competence, capturing how WOC operationalize culturally grounded care as a strategic, protective, and resistive praxis. By centering students as the moral and epistemic anchor of leadership decisions, this study demonstrates how relational, culturally sustaining practices can humanize bureaucratic systems, buffer harm, and advance systemic transformation in STEM higher education. These insights contribute to scholarship on culturally responsive leadership and provide a practical framework for advancing equity, inclusion, and empowerment in higher education contexts. Full article

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

The inclusion of children’s perspectives is increasingly emphasised in educational research; however, autistic children remain underrepresented as active participants, particularly in early educational contexts. This study synthesises methodological insights from two mixed-methods studies conducted in Swedish preschool and primary school settings to examine the conditions under which autistic children participate as contributors to research. A retrospective and reflexive methodological synthesis was employed, drawing on methodological documentation, video-recorded data, and researcher reflections from the original studies. The analysis examined how participation was designed, negotiated, and interpreted across contexts. The findings indicate that participation is not a binary feature of research design, but a graded, situated, and relational process shaped by accessibility, mediation, and contextual conditions. Accessibility functioned as an epistemic condition influencing what could be expressed and recognised as knowledge, while participation was continuously negotiated through interaction among children, adults, and research settings. Adult mediation both enabled and shaped participation, revealing inherent epistemic and ethical tensions. The study concludes that participation is more accurately understood as a methodological and relational accomplishment rather than an individual capacity, underscoring the need for flexible, reflexive research approaches that support diverse forms of expression while maintaining methodological quality and ethical integrity. 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

Polycyclic aromatic hydrocarbons (PAHs) are major carcinogenic pollutants in urban air, and inhalation exposure poses health risks, particularly for primary school children aged 6–14 years in school environments. Traditional deterministic models for incremental lifetime cancer risk (ILCR) assessment often fail to adequately quantify variability and epistemic uncertainty in exposure parameters. This study develops a multi-layered probabilistic framework that progresses from deterministic calculations through one-dimensional Monte Carlo and sensitivity-guided two-dimensional Monte Carlo to a hierarchical (second-order) two-dimensional Monte Carlo simulation. The hierarchical approach samples hyper-parameters of the input distributions (means, standard deviations, and modes) in the outer loop, while exposure variables are sampled in the inner loop using Latin hypercube sampling. Applied to PAH and BaP_eq concentrations measured indoors and outdoors during heating and non-heating seasons, the framework yielded mean total ILCR values of 1.42 × 10⁻⁶ for children and 1.18 × 10⁻⁶ for adults. The hierarchical 2D MC produced 95% confidence intervals on the 95th percentiles of [9.17 × 10⁻⁷, 5.67 × 10⁻⁶] for children and [6.48 × 10⁻⁷, 5.57 × 10⁻⁶] for adults, with outdoor heating identified as the dominant exposure pathway. Although the air sampling campaign was conducted in 2011–2012, the data remain representative for evaluating seasonal and microenvironmental variability of PAHs in urban school settings in the region, as PAH levels are predominantly driven by persistent combustion sources. This framework provides more comprehensive uncertainty quantification for complex environmental exposure scenarios. Full article