Search Results (359)

The increasing integration of Generative AI (GenAI) into higher education, particularly in the domain of language teaching, presents both opportunities and challenges. While AI-powered tools such as ChatGPT-5 can support language learning by generating personalised content which enables real-time interaction and feedback, they also risk perpetuating biases embedded in training data. These biases can appear in linguistic, cultural or socio-political forms, reinforcing stereotypes and influencing language norms. Therefore, equipping students and educators with strategies to critically assess AI outputs is essential for ethical and responsible AI use in language education. While recent research highlights the risks of algorithmic bias, less attention has been given to the perceptions and attitudes of pre-service teachers, whose future practice will shape classroom uses of these technologies. This exploratory pilot study adopts a survey-based approach to examine pre-service teachers’ baseline awareness of bias in artificial intelligence, with particular attention to linguistic and cultural dimensions Data were collected through an online questionnaire administered to 65 undergraduate students enrolled in Primary Education degree programmes. The study documents baseline perceptions prior to any instructional intervention and provides preliminary empirical evidence to inform the future design of pedagogical strategies aimed at developing critical AI literacy in teacher education. Full article

Background: Artificial intelligence (AI) holds transformative potential for mental health services. However, existing reviews have predominantly focused on algorithmic accuracy, with limited attention to how these technologies are implemented and integrated into real-world service delivery. This scoping review addresses this gap by examining the contexts in which AI technologies—including large language models (LLMs) and machine learning—are implemented, as well as the factors influencing their sustainable adoption within real-world mental health service systems. Methods: Following the established methodological framework, a systematic search (2015–2026) was conducted in PubMed and Scopus. Two independent reviewers screened an initial pool of 829 records using Zotero and Rayyan to minimize selection bias. Following title, abstract, and full-text screening based on predefined eligibility criteria, 26 studies focusing on real-world AI applications (e.g., clinical settings, community services, and case management) were included in the final synthesis. Results: The findings indicate a rapid acceleration in research, with 50% of included studies (n = 13) published since 2024. AI-driven decision support systems were the most prevalent (50%, n = 13), followed by predictive machine learning models (27%) and generative AI applications (15%). Most tools were designed for clinician use (77%) and implemented in hospital-based settings (46%). Although 46% of studies reported real-world implementation, more than half remained at the pilot stage. Notably, research emphasis has shifted from technical efficacy toward feasibility, and implementation contexts (n = 17). Conclusion: AI in mental health is transitioning from laboratory validation to real-world integration. However, the current landscape remains heavily centered on clinician workflows and screening functions, with limited expansion into community-based recovery and long-term prevention. To move beyond the pilot stage, future initiatives should prioritize seamless workflow integration and the application of structured ethical and implementation frameworks that support clinician–patient relationships. This review provides an evidentiary basis for advancing sustainable, AI-enhanced mental health service delivery. Full article

Artificial intelligence (AI) has become a significant paradigm of methodology and epistemology in the social sciences. Machine learning (ML), natural language processing (NLP), and generative models enable researchers to work with big, multimodal datasets, identify complex patterns, and recreate events in the social world in ways that previously were not feasible. At the same time, these innovations also lead to ethical challenges related to algorithmic bias, black boxes, data extractivism, and reinforced structural inequalities in welfare, government services, education, and criminal justice. The article critically questions the social sciences in the light of AI on three dimensions that are inextricably linked, namely: (1) the opportunities that AI provides to social-scientific inquiry; (2) the biases and constraints generated through data, models, and institutional application; and (3) ethical pathways that are necessary for the responsible governance of AI-facilitated research and decision support. The article is based on a scoping, critical thematic review of the recent literature, and its conceptualization of AI as a socio-technical infrastructure is that it produces knowledge and, at the same time, offers power. It explains the impact AI practices have on restructuring disciplines like sociology, psychology, political science, and policy analysis, and how it blindly predicts how data practices, design choices, and governance arrangements can either preserve or destroy existing hierarchies. The paper suggests an analytical framework synthesizing AI practices, social research practices, and governance structures in ethical frameworks. It argues that the emancipatory promise of AI in the social sciences is dependent on the attainment of something beyond principle-based claims of so-called ethical AI by operational governance mechanisms that make systems visible, debatable, and responsible in their respective situations. Full article

This exploratory study analyzes the relationship between cooperative oral reading and intercultural competence within the field of teacher education (i.e., training of pre-service FL teachers in primary education) at the University of Cádiz (Spain), aiming to move beyond traditional, Eurocentric conceptions of interculturality, by aligning the framework with the United Nations Sustainable Development Goals (SDGs), particularly SDGs 4, 5, 10, and 16. A mixed-methods design is adopted, combining quantitative and qualitative approaches through cooperative oral reading activities based on selected literary texts in English, French, and German addressing diversity, identity, inclusion, among others. Data are collected via recording forms administered to language assistants and two focus groups involving students and language assistants. The quantitative indicators of the study suggest that cooperative oral reading may contribute to foreign language learning, strengthen engagement between students and assistants, promote collaborative dialogue, and provide opportunities to challenge stereotypes, while interaction with native speakers (i.e., assistants) deepens understandings of cultural diversity and identity. Overall, the research proposes that cooperative oral reading is an illustrative pedagogical strategy for fostering inclusive intercultural competence and that linking classroom practices to the SDGs can contribute not only to language development but also to broader goals of equity, inclusion, and social justice. Full article

Service robots have the potential to support cognitive and social well-being in long-term care facilities, yet their widespread adoption depends on intuitive interaction modalities that minimize user learning effort and the need for a technical expert on-ground. Spoken dialogue is a natural interface, and recent advances in large language models (LLMs) promise more flexible and engaging exchanges than traditional scripted systems. In this study, we implemented a modular speech-based architecture combining automatic speech recognition, text-to-speech synthesis, and a conversational agent capable of switching between a fully scripted and LLM-driven dialogue. The implemented architecture was embodied in a TIAGo robot (PAL Robotics) and tested to compare three conversational strategies: (1) scripted, pre-defined dialogue, (2) LLM-based free-form conversation, and (3) LLM-based conversation augmented with personal information provided through the prompt. Eighteen younger adults and eighteen older adults engaged in a five-minute interaction with the robot under all three conditions in a within-subject design, and subsequently completed the Almere model questionnaire. Across all subscales and both participant groups, differences between dialogue strategies were small and statistically non-significant, despite informal comments from several older participants indicating a perceived increase in intelligence or naturalness for the LLM conditions. The findings suggest that generative dialogue and basic personalization alone do not meaningfully shift perceived acceptance in brief, task-neutral encounters, underscoring the importance of longer-term deployment and functionally meaningful robot roles in future evaluations. Full article

Background/Objectives: With growing waves of migration, children speaking a home language different from the language of school literacy have become increasingly common in Western education systems. In this context, understanding and monitoring bilinguals’ reading development is crucial to inform both educational and clinical practices and ensure equitable services. The present study contributes to the literature by investigating learning patterns in single-word reading across primary school grades. Monolingual and bilingual children learning to read in an alphabetic orthography were examined. Methods: The sample consisted of 565 typically developing monolingual and bilingual primary school children from grades 1–5 (bilinguals = 162). Participants completed a computerised Lexical Decision task (LDT) recording accuracy and response times, and standardised tests of reading and cognition. A parental questionnaire was used to gather socio-demographic and linguistic information. Results: Response bias-corrected accuracy rates in the LDT revealed an increase in sensitivity across school years after correcting for potential confounds (SES, vocabulary, nonverbal intelligence). No significant effect of bilingualism was observed. Response times for correct responses also decreased consistently across grades after controlling for the same confounds. Although no significant main effect of bilingualism emerged, an interaction with grade revealed a greater decrease in response times for second-grade bilinguals compared to monolingual peers. Conclusions: Monolingual and bilingual children showed comparable sensitivity rates and reading times, suggesting similar decoding skill acquisition. However, an earlier decrease in response times for bilinguals points to a facilitatory effect in the early stages of reading development, consistent with a bilingual advantage during skill learning. Full article

Traditional approaches to university-level history education often fail to provide immersive and interactive environments that foster deep cognitive engagement. To address these limitations, we developed an AI-enabled virtual museum system that integrates AI-generated content with knowledge graphs through a multi-layered architecture. The system architecture follows a three-tier framework: a front-end interaction layer (Unity/Unreal Engine) for real-time user engagement, a core service layer for intelligent event scheduling and response control (Chat General Language Model/Stable Diffusion), and a data and model layer (My Structured Query Language/MongoDB) to provide structured knowledge. To evaluate the system’s effectiveness, a four-week controlled experiment was conducted with 83 university students. The experimental group using the AI virtual museum showed a significantly higher mean post-test score (84.5 ± 6.8) than that of the control group (71.6 ± 7.9), with statistical significance at p < 0.001, starting from nearly identical baseline scores (61.2 and 60.4 for the experimental and control groups). Correlation analysis was conducted to identify scenario simulations (r = 0.59) and deep inquiry tasks (r = 0.54) as key drivers of learning mastery. By aligning advanced system engineering with educational theory, the results of this study offer a solution for high-fidelity, intelligent digital educational platforms, proposing a validated model for integrated system innovation in education. Full article

Query–service matching in customer service systems faces a critical challenge of accurately aligning user queries expressed in colloquial language with formally defined services while balancing business objectives. Traditional keyword-based and embedding approaches fail to capture complex semantic nuances and cannot provide interpretable explanations. We address this problem by proposing a novel reasoning-enhanced framework that leverages large language models (LLMs) for structured multi-criteria evaluation. Our key innovation is a reasoning-first scoring architecture where the model generates detailed explanations before numerical scores, reducing score variance by 18% through conditional mutual information. We introduce a controlled stochastic perturbation mechanism with theoretically derived optimal parameters that balance diversity and relevance, alongside a knowledge distillation pipeline enabling 960× model compression (480B→0.5B parameters) while retaining 94% performance. Rigorous theoretical analysis establishes Pareto optimality guarantees for multi-criteria evaluation, information-theoretic entropy reduction bounds, and PAC learning guarantees for distillation. Experimental validation on real-world telecommunications data demonstrates 89% Precision@1 (15.3% improvement over baselines), 23% diversity enhancement, and 96× latency reduction, with deployment cost decreasing 1200× compared to direct LLM inference. This work bridges the gap between LLM capabilities and production deployment requirements through principled mathematical foundations and practical system design. Full article

Smart cities require effective disaster management (like flooding, solar storms, sandstorms, or hurricanes), as it directly impacts people’s lives. The key challenges of disaster management are timely detection and effective notification during the crisis. This research presents a smart multi-layer framework for notification classification and management before and during flooding disasters. The framework includes an early detection module as the main phase in the alerting process. This step depends on an Ensemble Learning (EL) model based on a triad of the three best selected models (Deep Learning (DL), Random Forest (RF), and K-nearest Neighbor (KNN)) to analyze data collected continuously from the Internet of Things (IoT) layer. In the boosting phase, the framework utilizes Large Language Models (LLMs) with DL to analyze social textual crowdsourcing data. The results will enable the framework to identify the most affected areas during a flood. The framework adds a fog computing layer alongside a cloud layer to enable instantaneous processing of user responses and generate specialized alerts based on contextual factors such as location, time, risk level, alert type, and user characteristics. Through testing and implementation, the proposed algorithms demonstrated an accuracy rate of over 98% in detecting threats using a dataset of real, collected weather and flooding data. Additionally, the framework proposes a centralized control panel and a design of a smartphone application that offers essential services and facilitates communication among managed civil defense teams, citizens, and volunteers. Full article

Phishing websites pose a major cybersecurity threat, exploiting unsuspecting users and causing significant financial and organisational harm. Traditional machine learning approaches for phishing detection often require extensive feature engineering, continuous retraining, and costly infrastructure maintenance. At the same time, proprietary large language models (LLMs) have demonstrated strong performance in phishing-related classification tasks, but their operational costs and reliance on external providers limit their practical adoption in many business environments. This paper presents a detection pipeline for malicious websites and investigates the feasibility of Small Language Models (SLMs) using raw HTML code and URLs. A key advantage of these models is that they can be deployed on local infrastructure, providing organisations with greater control over data and operations. We systematically evaluate 15 commonly used SLMs, ranging from 1 billion to 70 billion parameters, benchmarking their classification accuracy, computational requirements, and cost-efficiency. Our results highlight the trade-offs between detection performance and resource consumption. While SLMs underperform compared to state-of-the-art proprietary LLMs, the gap is moderate: the best SLM achieves an F1-score of 0.893 (Llama3.3:70B), compared to 0.929 for GPT-5.2, indicating that open-source models can provide a viable and scalable alternative to external LLM services. Full article

Fog computing extends cloud services to the network edge to support low-latency IoT applications. However, since fog environments are distributed and resource-constrained, intrusion detection systems must be adapted to defend against cyberattacks while keeping computation and communication overhead minimal. This systematic review presents research on intrusion detection systems (IDSs) for fog computing and synthesizes advances and research gaps. The study was guided by the “Preferred-Reporting-Items for-Systematic-Reviews-and-Meta-Analyses” (PRISMA) framework. Scopus and Web of Science were searched in the title field using TITLE/TI = (“intrusion detection” AND “fog computing”) for 2021–2025. The inclusion criteria were (i) 2021–2025 publications, (ii) journal or conference papers, (iii) English language, and (iv) open access availability; duplicates were removed programmatically using a DOI-first key with a title, year, and author alternative. The search identified 8560 records, of which 4905 were unique and included for qualitative grouping and bibliometric synthesis. Metadata (year, venue, authors, affiliations, keywords, and citations) were extracted and analyzed in Python to compute trends and collaboration. Intrusion detection systems in fog networks were categorized into traditional/signature-based, machine learning, deep learning, and hybrid/ensemble. Hybrid and DL approaches reported accuracy ranging from 95 to 99% on benchmark datasets (such as NSL-KDD, UNSW-NB15, CIC-IDS2017, KDD99, BoT-IoT). Notable bottlenecks included computational load relative to real-time latency on resource-constrained nodes, elevated false-positive rates for anomaly detection under concept drift, limited generalization to unseen attacks, privacy risks from centralizing data, and limited real-world validation. Bibliometric analyses highlighted the field’s concentration in fast-turnaround, open-access journals such as IEEE Access and Sensors, as well as a small number of highly collaborative author clusters, alongside dominant terms such as “learning,” “federated,” “ensemble,” “lightweight,” and “explainability.” Emerging directions include federated and distributed training to preserve privacy, as well as online/continual learning adaptation. Future work should consist of real-world evaluation of fog networks, ultra-lightweight yet adaptive hybrid IDS, self-learning, and secure cooperative frameworks. These insights help researchers select appropriate IDS models for fog networks. Full article

Generative Artificial Intelligence (AI) has transitioned from a research milestone to a general-purpose technology with wide-ranging implications for organizations, labor markets, and information systems. Thanks to improvements in deep learning, generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, transformer-based language models, and reinforcement learning from human feedback (RLHF), generative AI can now create high-quality text, images, audio, code, and other types of content. This review synthesizes the core technical foundations and best practices for training, evaluation, and governance, with an emphasis on scalability and human oversight. The paper examines applications across customer service, marketing, software development, healthcare, finance, law, logistics, and the creative industries, and assesses the labor implications of generative AI using a sociotechnical lens. This study also develops a disruption index that integrates task exposure, adoption rates, time savings, and skill complementarity. The paper concludes with actionable recommendations for policymakers, organizations, and workers, emphasizing the importance of reskilling, algorithmic transparency, and inclusive innovation. Taken together, these contributions situate generative AI within broader debates about automation, augmentation, and the future of work. Full article

In modern organizations, IT Service Management (ITSM) relies on the efficient handling of large volumes of unstructured textual data, such as support tickets and incident reports. This study investigates the automated classification of IT support requests as a data-driven decision-support task within a real-world enterprise ITSM context, addressing challenges posed by multilingual content and severe class imbalance. We propose an applied machine-learning and natural language processing (NLP) pipeline combining text cleaning, stratified data splitting, and supervised model training under realistic evaluation conditions. Multiple classification models were evaluated on historical enterprise ticket data, including a Logistic Regression baseline and transformer-based architectures (multilingual BERT and XLM-RoBERTa). Model validation distinguishes between deployment-oriented evaluation on naturally imbalanced data and diagnostic analysis using training-time class balancing to examine minority-class behavior. Results indicate that Logistic Regression performs reliably for high-frequency, well-defined request categories, while transformer-based models achieve consistently higher macro-averaged F1-scores and improved recognition of semantically complex and underrepresented classes. Training-time oversampling increases sensitivity to minority request types without improving overall accuracy on unbalanced test data, highlighting the importance of metric selection in ITSM evaluation. The findings provide an applied empirical comparison of established text-classification models in ITSM, incorporating both predictive performance and computational efficiency considerations, and offer practical guidance for supporting IT support agents during ticket triage and automated request classification. Full article

Artificial Intelligence (AI) holds transformative potential to revolutionize teaching and learning, yet its rapid integration poses significant challenges for teacher preparation. While AI competencies—encompassing knowledge, skills, and attitudes—are critical for effective integration, limited research has holistically addressed these three interconnected domains. To bridge this gap, this quasi-experimental study (N = 259) evaluated a triadic instructional design synergizing the intelligent technological, pedagogical, and content knowledge (Intelligent-TPACK) framework, Synthesis of Qualitative Data model, and curated AI tools. Pre-service English as a foreign language (EFL) teachers were assigned to an experimental group (n = 137) receiving the structured intervention or a control group (n = 122) engaging in self-directed AI exploration. Results reveal that the experimental group achieved greater gains across all Intelligent-TPACK dimensions and demonstrated higher-order AI applications in lesson planning. Furthermore, the experimental group experienced a significant reduction in perceived pressure and reported higher perceived usefulness regarding AI integration. Qualitative data revealed that hands-on AI tasks enhanced participants’ confidence, yet challenges with prompts and critical adaptation persisted. The findings demonstrate that systematic training is essential for transforming pre-service teachers’ passive awareness into competent AI integration. Finally, this paper proposes practical implications for integrating this triadic framework into teacher education curricula to facilitate sustainable AI adoption. Full article

This study presents an AI-based, human-in-the-loop decision support system designed for large-scale institutional query routing and response generation. The proposed system combines semantic text classification with large language model-based response generation to assist administrative staff in handling high-volume natural language requests from various system users, while preserving human oversight. Using a dataset of 135,359 real student and staff interactions collected over 15 years, the system was designed, deployed, and evaluated in a live university information portal. The classification component achieved 95.88% accuracy in evaluation and 82.21% staff acceptance in practice, while 94.81% of AI-generated draft responses were adopted with minor edits. Operational evaluation showed a 30.8% reduction in resolution time, a 32.6% decrease in misrouting, and an increase in user satisfaction from 3.6 to 4.9 out of 5. The system is implemented as a modular RESTful API to ensure interoperability with existing Student Information Systems, with analysis code available upon request to support replication in similar resource-constrained environments. The results illustrate how human-in-the-loop AI systems can support improvements in service quality, efficiency, and institutional capacity in resource-constrained environments, providing a transferable applied AI framework for scalable decision support in complex administrative domains. Full article