Search Results (109)

This paper offers a preliminary conceptual mapping of maxxing culture by treating “-maxxing” not as a series of isolated internet trends but as a platform-native grammar of self-optimization. The argument distinguishes three interacting dimensions: linguistic, through a productive suffix that can attach to a wide range of domains; cultural-processual, through an ideal-typical cycle of diagnosis, intervention, evaluation or display, and escalation; and platform-mediated, through systems of visibility, comparison, metrics, recommendation, and monetization. The paper situates maxxing within longer histories of self-help, self-tracking, neoliberal self-management, gaming-derived min-maxing, and platformed language. It develops a typology of maxxing variants, identifies five modes of optimization, and traces the suffix’s movement from subcultural jargon into wellness markets, corporate discourse, policy language, and state communication. The analysis also examines AI face-rating apps as a productized illustration of the maxxing cycle and considers how clinical accounts of body-image harm complement a cultural analysis of the platforms, markets, and norms that can shape and reward such behavior. Particular attention is given to how maxxing can reproduce inequalities of gender, race, and class while presenting optimization as broadly available. Drawing on a conceptual synthesis and critical cultural mapping of academic research, public online discourse, journalism, institutional materials, Google Trends data, and selected platform, app, and commercial materials, the paper argues that maxxing can turn selfhood into a modular, competitive, and perpetually incomplete project. Full article

Background: Human immunodeficiency virus (HIV) remains a major public health challenge in sub-Saharan Africa, where linguistic diversity and limited digital health resources constrain patient education and antiretroviral therapy (ART) adherence. Amharic, one of Africa’s most widely spoken languages, remains underrepresented in clinically oriented conversational AI systems. Methods: We developed SmartHIVCare, a bilingual (Amharic–English) multimodal conversational system integrating retrieval-augmented generation, multilingual semantic retrieval, Amharic-specific text normalization, and speech-based interaction for ART education and adherence support. Performance was evaluated using automated response quality metrics, clinician-based human evaluation, latency analysis, error analysis, and an ablation analysis on 50 bilingual queries (25 English, 25 Amharic). Results: SmartHIVCare achieved BLEU scores of 0.448 ± 0.386 (95% CI: 0.341–0.555) and a BERTScore of 0.820 ± 0.114 (95% CI: 0.789–0.852). Human evaluation yielded high ratings for accuracy (4.8/5, 4.6/5), clarity (4.6/5, 4.6/5), safety (4.6/5, 4.6/5), and clinical usefulness (4.8/5, 4.4/5) for English and Amharic, respectively. Response latency scaled with modality: 3.2 s (text-to-text), 4.1 s (text-to-speech), 5.8 s (speech-to-text), and 6.4 s (speech-to-speech). Conclusions: SmartHIVCare demonstrates the feasibility of retrieval-grounded bilingual conversational AI for HIV education in underrepresented languages. This proof-of-concept evaluation focuses on technical feasibility and response quality and requires validation through larger real-world clinical studies. Full article

This article maps research on artificial intelligence (AI) in Christian Religious Education (CRE) during the first phase of rapid development following the widespread adoption of generative AI. A scoping review was conducted in Scopus, Web of Science, the Open Digital Theological Library, and IxTheo, supplemented by Google Scholar. Included were full-text English-language publications from 1 January 2023 to 31 March 2026 that addressed AI in Christian educational, catechetical, theological–educational, or formative contexts. The final corpus comprised 23 publications. Data were charted by publication type, geographical and educational context, main topic, and key claims or findings. The results show that the field is recent, methodologically heterogeneous, and not yet well consolidated, but that the emerging debate is organised around four inter-related strands: didactic potential and personalisation, ethical–technical limitations, anthropological–theological reflection, and systemic, institutional, and cultural–geographical conditions. The literature is marked by a tension between AI’s didactic potential and concerns regarding its ethical, theological, anthropological, and institutional implications. The review contributes a thematic synthesis that clarifies why AI integration in CRE requires not only educational innovation but also theologically informed, pedagogically mediated, and context-sensitive frameworks for religious formation. Significant gaps remain, including limited empirical and longitudinal research, insufficient attention to students’ and parents’ perspectives, cultural and linguistic contexts, and a lack of religion-specific, empirically tested didactic models. The review concludes that future research should move toward longitudinal, practice-based, and theologically grounded models for AI use in CRE. Full article

This study investigates the educational potential of instructor-designed Generative Pre-trained Transformers (GPTs) in a university-level Japanese course, drawing on self-determination theory (SDT) and the noticing hypothesis. Using a mixed-methods design, we examined how the continuous use of an instructor-developed Artificial Intelligence tutoring system (basic Japanese GPTs) relates to learners’ psychological needs satisfaction, cognitive noticing, and perceptions of Artificial Intelligence (AI)-assisted learning among 74 undergraduate students at a South Korean university. Quantitative data were analyzed using descriptive statistics and Pearson correlation; qualitative data from open-ended items and reflective writing underwent systematic content analysis. The findings revealed three key patterns. First, learners reported relatively high levels of satisfaction across all three SDT needs—autonomy, competence, and relatedness—particularly in relation to self-directed reviews and affective safety. Second, qualitative analysis identified three distinct noticing experiences: AI-supported clarification of linguistic form, noticing through intentional error generation and AI feedback, and metacognitive regulation of learning strategies. Third, the learners perceived the instructor-designed GPTs not merely as a convenience tool but as a structured learning environment that supported output-oriented, interaction-based practice. These findings suggest that the educational effectiveness of generative AI in foreign language education is not determined by frequency of use alone but also by the quality of pedagogical design underlying its deployment. This study contributes a practice-based model for AI integration in general education language courses while acknowledging limitations related to its single-course scope and reliance on self-reported data. Full article

Sequencing artificial intelligence (AI) investment across requirements engineering (RE) phases remains an open problem in legacy system modernisation, particularly when expert judgements include hesitancy. This study prioritises five phases of the AI-Augmented Requirements Engineering model using spherical fuzzy analytic hierarchy process (SF-AHP) criteria weighting followed by direct spherical fuzzy phase scoring. Experts selected linguistic terms mapped to predefined spherical fuzzy triplets; the coordinates were represented explicitly but not elicited separately. Continuous spherical aggregates were converted through a calibrated reciprocal exponential map, while linguistic labels were retained only for interpretation. The selected score factorises into an antisymmetric polarity term and a hesitancy-dependent modulation term. We show that score antisymmetry, combined with reciprocal rescaling, preserves the AHP reciprocity imposed by the reflection convention. In a banking transformation involving three experts, Defect Triage and Test Scenario Generation formed the leading tier, while TO-BE Design ranked last. The within-pair margin was 0.0079. The ordering reversed in 18 of 120 single-step perturbations and was retained in 57.5% and 55.0% of the Monte Carlo instances at the two noise levels, respectively. Ablation and sensitivity analyses identify which modelling choices affect ranking stability. The results support tier-level prioritisation rather than a strict adoption sequence. Full article

In Multi-Criteria Group Decision-Making (MCGDM), the assignment of weights to decision-makers is a crucial but methodologically delicate step, especially when the group includes both human experts and artificial experts such as intelligent agents, Artificial Intelligences (AIs) or Large Language Models (LLMs). Existing weighting strategies are often either difficult to interpret or poorly suited to heterogeneous groups of evaluators. In this paper, we investigate a fuzzy rule-based approach to expert weighting, building on a previously introduced methodological framework and focusing here on its application-oriented validation. The proposed method models expert weighting as a Fuzzy Rule-Based System (FRBS) in which the relevant properties of the experts are represented by linguistic variables and combined through interpretable IF–THEN rules. In this way, weighting policies can be expressed transparently and adapted to the requirements of the decision domain. The framework produces normalised weights in the interval $[0,1]$, which can then be incorporated into standard MCGDM aggregation procedures. To assess the operational behaviour of the approach, we consider an application involving the weighting of four open-source LLMs (apertus:8b, gemma4:e4b, mistral-small3.2:24b, and nemotron-cascade-2:30b) over three multilingual criteria (English, Italian, Portuguese) and two resource-side criteria (VRAM, open-sourceness), each modelled by three trapezoidal fuzzy sets and combined into a five-class output partition; the underlying dataset is built from 10 independent repetitions of 100 questions per model. Under a language-focused rule base of five IF–THEN rules, the four experts receive sharply separated normalised weights $(0.003,0.149,0.301,0.548)$—a top-to-bottom ratio above 180—whereas a combined linguistic/resource-aware rule base of five rules flattens the distribution to $(0.227,0.360,0.222,0.191)$ and selects a different winner, demonstrating that policy changes are encoded explicitly in the output. A 100-run Kendall’s $\tau$ perturbation analysis confirms that the induced rankings remain stable under moderate input noise, particularly for the language-focused policy, while substituting the Product t-norm with Gödel or Lukasiewicz leaves the language-focused ranking invariant but induces rank reversals in the more discriminative resource-aware policy. A comparison against three independent baselines (Markov Logic Networks, ProbLog, TOPSIS) shows that ProbLog reproduces the FRBS ordering in both case studies, MLN compresses the normalised scores under its global probabilistic interaction, and TOPSIS diverges whenever conditional IF–THEN preferences must be encoded. A worked end-to-end aggregation example with three alternatives, three criteria, and four experts further shows that the FRBS weights propagate into a clear selection of the best alternative, with aggregated scores $(S_1,S_2,S_3)=(8.88,6.78,6.21)$. These results confirm both the practical usability of the method and its suitability for contexts in which multiple, potentially competing, objectives must be balanced explicitly. Overall, the paper provides an application-oriented study of an FRBS-based weighting scheme for artificial experts, highlighting its interpretability, adaptability, and potential relevance for contemporary MCGDM settings. Full article

More than 43 million children are currently displaced globally, yet the artificial intelligence (AI) systems deployed in their education are rarely designed with cultural identity, linguistic continuity, or belonging as architectural concerns. Existing frameworks treat identity as a contextual consideration surrounding educational technology; this paper argues that identity should be treated as a design variable within AI system architecture itself. The paper develops the AI-Based Identity Support Framework (AISF), a conceptual model constructed through interdisciplinary synthesis across bodies of literature including cultural bereavement theory, narrative identity, ethical AI design, and multilingual AI systems, and informed by the author’s practitioner engagement with displaced communities (2018–2023). The AISF specifies three interlocking domains (cultural anchoring, emotional co-construction, and adaptive belonging), each defined operationally and accompanied by diagnostic indicators, design principles, and a preliminary assessment rubric. The framework is differentiated from trauma-informed pedagogy, culturally sustaining pedagogy, and principle-based AI ethics as a system-architectural rather than pedagogical construct. The paper engages critically with risks internal to identity-supportive AI architectures, including essentialization, surveillance, and algorithmic identity-fixing, and addresses low-resource deployment constraints. The AISF is intended to provide a structured diagnostic instrument for designers, evaluators, and policymakers, aligned with Sustainable Development Goals 4.7, 10, and 16. Full article

Detecting AI-generated text has become a critical task as artificial intelligence systems are increasingly used in content creation. Current detection methods often suffer from limited accuracy and weak multilingual performance. This problem is especially challenging in Turkish, Arabic, and English due to their distinct linguistic structures, including agglutinative morphology in Turkish, root-based morphology in Arabic, and semantic ambiguity in English. To address these challenges, this study proposes a hybrid architecture that combines a Transformer-based DistilBERT model with a Graph Convolutional Network (GCN). While DistilBERT captures rich contextual and semantic information, GCN enhances detection by modeling structural relationships within text data. The proposed model is evaluated against other well-known approaches. Experimental results show that the hybrid DistilBERTGCN framework achieves high detection accuracy, reaching 99% for English and 98% for Turkish and Arabic. In addition, this study introduces new multilingual datasets, contributing to the advancement of the literature research. Full article

Background: Inequitable access to clinical trials persists in blood cancers despite ongoing equity, diversity, and inclusion (EDI) efforts. Despite the critical role of clinical trials in improving survival and outcomes, recruitment remains suboptimal, limiting patient access to potentially life-saving therapies. Practical and scalable approaches are therefore needed to address the non-medical barriers that hinder patient readiness upstream of enrolment. Methods: Myeloma Canada led a national, multi-phase initiative using human-centred design (HCD) to operationalize EDI in clinical trials. Following an initial systems level workshop, the two-day Phase 0 workshop used a HCD approach that convened a purposively selected multidisciplinary group of stakeholders to co-design operational solutions for non-medical barriers affecting trial participation for patients. Given the use of purposive sampling, the results should be interpreted as reflecting a balanced range of diverse, informed perspectives across the Canadian clinical trial ecosystem. Results: Participants identified persistent cultural, logistical, financial, and linguistic barriers, along with fragmented awareness of available supports. Across diverse personas and care settings, all groups independently converged on a human-centred, equity-focused pre-trial navigation model supported by simple digital tools, including AI-enabled infrastructure drawing on curated resources from validated sources with appropriate governance, privacy, and oversight. Digital tools were proposed to support, rather than replace, human support and to align with existing health system realities. Conclusions: This hypothesis-generating work proposes a feasible, sustainable, and scalable equity-embedded, protocol-agnostic navigation framework. Its external hub-and-spoke structure can reduce non-medical barriers, strengthen trial access and accrual, and enhance representativeness. Pilot implementation that assesses feasibility, uptake, workflow impact, equity effects, and implementation burden is warranted. Full article

Fake news detection has become a major research topic at the intersection of artificial intelligence, data mining, and information security. In this paper, we evaluate the performance of English-trained algorithms on English translations of Romanian-sourced news articles, using a translation-mediated cross-domain evaluation design. The study is based on source code generated with the assistance of artificial intelligence systems for a set of machine learning and transformer-based models. The code was subsequently implemented in Google Colab. 2026, trained on international benchmark datasets, and tested on Romanian news content. This design allowed the rapid prototyping of multiple detection pipelines and the systematic observation of their behavior in a media environment different from that represented in the training corpora. The models were evaluated comparatively using standard classification metrics, including accuracy, precision, recall, and F1-score, complemented by additional indicators relevant to model robustness and practical usability. The experimental results revealed significant differences in performance across algorithms when applied to English translations of Romanian-language news content after training on international datasets. However, this study does not provide a direct comparison between model performance on the international benchmark datasets and the Romanian test corpus; therefore, the gap between the international training corpus and the Romanian-sourced test corpus is interpreted as an exploratory limitation and as a direction for future research. Based on these findings, we propose an empirical classification of the tested models according to their predictive effectiveness, their contextual robustness across linguistic environments, and their operational relevance as filtering tools for institutional monitoring. The results show that AI-assisted coding workflows can provide a viable starting point for reproducible misinformation research, but they also underline the limitations of directly transferring models trained on non-Romanian data to local media ecosystems. The study offers both a replicable evaluation framework and practical insights for institutions involved in strategic communication, public security, and the monitoring of information threats. Full article

Background: Speech models (wav2vec 2.0, HuBERT, Whisper), large language models (GPT, LLaMA), and conversational AI have expanded computational speech analysis from handcrafted acoustic features to dialogue-based neurological assessment. How well these approaches address clinical practice has not been evaluated. Methods: We conducted a narrative review searching PubMed, Google Scholar, and IEEE Xplore, supplemented by Interspeech and ICASSP proceedings. Findings are organized along three layers: acoustic-motor (voice quality, prosody, articulation), language-transcript (lexical, syntactic, semantic, and discourse analysis), and integrated multimodal-conversational (interactive dialogue systems). Traditional acoustic biomarkers provide background; the primary focus is on foundation models, LLMs, and conversational AI. Findings: Speech foundation models outperform handcrafted features on several classification tasks but degrade on severely impaired speech due to domain mismatch with healthy training data. LLMs classify transcripts and score cognitive tests, but operate on text alone and cannot access acoustic-motor information. Conversational AI can administer cognitive screening through naturalistic dialogue, but validation is limited to small single-centre feasibility studies. Prospective clinical validation remains limited. Cross-linguistic generalizability is untested for most methods. Interpretation: The field is moving toward integrated speech-language assessment, but the gap between technical capability and clinical utility remains wide. Closing it requires diverse multilingual datasets, standardized benchmarks, prospective validation, and ethical governance. Full article

This article develops a conceptual account of semantic displacement in conversational AI. The central question concerns how agency is affected when systems do more than automate information retrieval and begin to supply the descriptions, classifications and normative cues through which users understand what they are doing. Drawing on philosophy of action, philosophy of language, hermeneutics, philosophy of technology and critical accounts of algorithmic mediation, this article reconstructs the relation between meaning and action as a condition of agency. Its methodological approach is conceptual and diagnostic, oriented toward clarifying a problem that becomes visible when established theories are brought together in relation to contemporary conversational systems. The article interprets these systems as operational semantic infrastructures that organize context-sensitive linguistic uptake within practical environments such as health, work, education, administration and everyday self-management. It then introduces semantic displacement as the condition in which action-relevant meanings become increasingly organized, prioritized and consolidated outside the agent’s own participatory interpretation. The argument contributes a vocabulary for distinguishing agency-enhancing semantic support from forms of semantic substitution that weaken interpretive participation. It concludes by proposing semantic sovereignty and interpretive contestability as normative ideals for human agency in AI-mediated environments. The argument specifies action as intentional conduct understood under socially available descriptions and cognition as situated interpretive sense-making rather than purely internal computation. It also clarifies three conditions under which semantic support becomes displacement: opaque semantic generation, practical stabilization and reduced interpretive contestability. Full article

The governance of hate-related online communication increasingly relies on artificial intelligence, yet the scientific landscape linking computational detection methods with regulatory and ethical frameworks remains fragmented. This study provides a systematic bibliometric analysis of scholarship on the application of artificial intelligence to hate speech detection in digital environments, focusing on literature that examines hate speech and online hate through artificial intelligence, machine learning, deep learning, natural language processing, and other automated detection techniques. A dataset of 2137 publications indexed in Scopus between 2013 and 2026 was constructed and analyzed using the bibliometrix package in R. Descriptive indicators, thematic mapping, keyword co-occurrence analysis, citation structures, and temporal trend analysis were employed to examine the field’s conceptual organization, methodological evolution, and publication dynamics. The results reveal rapid annual growth, strong interdisciplinary collaboration, and a research structure dominated by language-processing methodologies, with natural language processing, machine learning, and deep learning constituting the central analytical infrastructure. Temporal patterns indicate a progression from dataset construction and feature engineering toward neural architectures, transformer models, and increasing attention to multilingual challenges. Overall, the findings indicate that the field remains predominantly oriented toward the development of scalable computational detection systems, while governance-related concerns, such as transparency, accountability, and linguistic inclusivity, emerge as structurally secondary, albeit increasingly salient, dimensions within the broader AI-centered research landscape. Full article

Digital Transformation (DT) increasingly relies on project-based organizing to develop and deploy new capabilities, yet corporate innovation projects frequently stall not for lack of ideas but because of recurring governance and resource-commitment bottlenecks. This study presents a micro-longitudinal, AI-enabled, and human-reviewed analysis of 711 episodes drawn from 28 weekly project governance meetings across two corporate startup initiatives participating in the same internal incubation program, conducted between November 2024 and April 2025. Employing a six-stage analytical pipeline that combines episode-level segmentation, linguistic tension markers, and a large language model (LLM) classifier, we identify 28 decision-relevant governance tensions, which are then abductively grouped into 13 project governance dilemmas and mapped onto Teece’s dynamic capabilities framework (sensing, seizing, reconfiguring). The key finding is that 62% of dilemmas are structural in nature—reflecting persistent governance design tensions between autonomy and control, compliance and agility, and centralization and decentralization—and that 69% concentrate at the seizing stage, corresponding to resource-commitment and execution decisions. This pattern indicates a governance choke point in corporate DT projects that is structural and decisional rather than ideational. By shifting attention from lagging indicators (overruns) to governance tension leading indicators, the approach supports earlier interventions to reduce decision latency and protect project delivery performance. We further synthesize two incubation-specific meso-level governance dilemmas—stakeholder engagement and compliance vs. agility—that serve as transmission mechanisms between macro structural constraints and micro-level decision bottlenecks. The AI-enabled pipeline is proposed as a replicable early-warning system for project governance tensions in organizations pursuing digital transformation. Full article

AI systems are entering criminal proceedings as evidence producers, risk assessors, and decision shapers, yet the procedural architecture of adversarial and mixed systems was built on the assumption that evidence originates from human actors whose reasoning can be reconstructed and challenged. This article introduces the concept of evidentiary authority—the power to determine what counts as reliable evidence and how much weight it carries—and argues that this authority is migrating from human decision-makers to algorithmic systems without adequate procedural safeguards. The article draws on forensic linguistics and comparative criminal procedure to examine two domains where this migration is most visible: generative AI, which can fabricate or manipulate the texts on which forensic authorship analysis depends, and predictive AI, which feeds opaque risk scores into judicial decisions at stages where adversarial scrutiny is weakest. A structural phenomenon, which the article terms the “inferential catalyst”, is identified: AI outputs that shape proceedings without entering the formal evidence record. These two domains are tested against seven principles of criminal procedure: free evaluation of evidence, immediacy, judicial independence, the right to a reasoned decision, adversarial proceedings, the right of confrontation, and the presumption of innocence. At each principle, the same structural problem recurs: the system presupposes human reasoning that AI outputs cannot provide and that existing procedural mechanisms cannot compel. Six safeguards are proposed as conditions for admissibility: algorithmic transparency, independent auditing, defence access to algorithmic expertise, admissibility standards for algorithmic evidence, enhanced justification obligations, and capacity building. Full article