Search Results (260)

Automatic gesture and movement recognition techniques are mainly used with adults for various purposes in public, clinical, and laboratory settings. Growing interest in this field has led to the increasing application of these methods in child behavioural analysis to serve different societal and educational functions. However, manual human annotation of behaviours remains the predominant method, and only a limited number of studies have explored the use of automatic recognition for children. This review aims to evaluate the rapidly developing techniques of automatic gesture and movement recognition that focus on child behaviour analysis across different settings and for different purposes. More specifically, it analyzes their purposes, target groups, settings, accuracy, and limitations, as well as the ethical issues and data privacy frameworks that should be considered in child-centred AI. Using a systematic review approach following the PRISMA guidelines, this study examines research published between 2021 and 2025 in four databases: Web of Science (WoS), Scopus, PubMed, and IEEE Xplore. From a total of 27 included studies, the findings reveal that automatic gesture and movement recognition is being applied across multiple fields, with consideration of children’s developmental needs. However, a critical gap in technical reporting was identified: fewer than half of the included studies (44%) provided accuracy metrics or clinical validity. Furthermore, evidence of robust ethical safeguards remains limited. To support children’s well-being, future studies must bridge the lab-to-field gap, prioritize natural research settings and enforce ethical and data protection measures. Full article

The ethical dimensions of sustainability can be overlooked by academics and project professionals despite ethics being relevant to the achievement of United Nations (UN) Sustainable Development Goals (SDGs). A survey of United Kingdom (UK)’s construction industry leaders is used to identify ethical challenges and solutions, while highlighting the link between sustainability, ethics and individual behaviour. A Theory of Planned Behaviour (TPB) approach is employed to scope out a series of ethical scenarios via an analysis of people, work tasks, culture and training, and subsequently validated via an industry workshop. It is argued that while project tools and techniques fail to engage adequately with ethical issues (e.g., stakeholder management), a proactive examination of attitude, norm, control and intention by project managers at appropriate project times can assist with the identification of potential ethical issues: a TPB-based prompt sheet being presented to assist project managers with their ethics work. The paper makes an original contribution that highlights the relationship between sustainability, ethical working practices and UN SDGs. Despite the relevance of ethics to SDGs, no prior study has used TPB to model ethical scenarios in construction project management. Full article

Digital Twins and Living Labs are increasingly used to support conservation, safety, accessibility, and visitor experience in cultural-heritage sites. Their practical value, however, depends on interfaces that can explain heterogeneous evidence, expose provenance, and operate under public-sector governance constraints. This paper presents a Sovereign Conversational Assistant (SCA) for the Libelium Heritage Living Lab, implemented as a small-language-model (SLM) and retrieval-augmented generation (RAG) stack that combines curated heritage and operational knowledge bases with provenance logging, refusal controls, and language enforcement. We first compare the Spanish public model BSC-LT/ALIA-40b-instruct-2601 with mistralai/Mistral-Small-3.2-24B-Instruct-2506 using 19 canonical test conditions executed over 155 repeated runs across five categories: historical queries, client experience, data analysis, hallucination resistance, and safety/ethics. Mistral passed all repeated runs, whereas ALIA passed 129/155 runs, showing strong factual and visitor-information behaviour but weaker numerical analysis, cross-lingual safety, and Spanish-language enforcement. To address external validity, we add a non-sovereign baseline comparison over the 13 canonical prompts against claude-opus-4-7, gemini-3.5-flash, and gpt-5.5 under the same RAG-conditioned harness. In this prompt-level comparison, mean final scores were ALIA 0.963, Claude Opus 4.7 0.938, Gemini 3.5 Flash 0.892, GPT-5.5 0.877, and Mistral 0.871; no pairwise difference was significant after Holm correction, and ALIA was non-inferior to the best external baseline at margins of 0.05 and 0.10, whereas Mistral was not. The contribution is therefore not a new RAG algorithm, but an operational method for deploying and evaluating a governance-aware, sovereign assistant for cultural-heritage Digital Twins, together with evidence that sovereign models can be competitive in controlled heritage RAG tasks while still requiring larger, human-calibrated benchmarks before stronger claims are made. Full article

Tikanga Māori, the indigenous system of values, principles, and practices in Aotearoa New Zealand, offers a relational, ethics-based framework for responding to contemporary global challenges such as geopolitical instability, environmental degradation, and digital disruption. Dominant Western approaches, often grounded in individualism and resource extraction, are increasingly limited in addressing these issues. Drawing on Aotearoa New Zealand as a case study, tikanga is presented as a living system centred on interconnectedness, responsibility, and collective wellbeing. Examples from law, health, research ethics, and sport demonstrate how these values shape both institutional practice and everyday behaviour. Rather than functioning as a cultural add-on, tikanga provides an alternative way of understanding relationships between people, communities, and the environment. The analysis highlights the relevance of tikanga-informed approaches for rethinking governance, leadership, and sustainability in an increasingly complex and interconnected world. Full article

This systematic literature review (SLR) examines the evolving nature of blended learning (BL) in higher education from 2020 to 2025. While BL is widely promoted for its flexibility, accessibility, and inclusivity, its practical application is uneven. This review integrated findings from 63 peer-reviewed studies obtained from five major academic databases and employed the PRISMA 2020 protocol to ensure methodological transparency and research quality. Findings were analysed using the Technological Pedagogical Content Knowledge (TPACK) model and the AI Roles Framework to assess instructional design and technology integration. This analysis was conducted through a thematic approach to identify recurring patterns and key insights across the literature on BL and the use of emerging technologies such as AI, AR/VR, and learning analytics within BL. The study identifies three main challenges. There is an excessive application of tool-centric models such as TAM and UTAUT. Engagement with educators, administrators, and under-represented learners is limited. Ethical considerations related to the use of AI, AR/VR, and learning analytics are often overlooked. The most common issue is the higher dependence on models such as TAM and UTAUT. The review also shows that many educational applications reproduce similar design patterns and content structures, while insufficiently responding to learner diversity, digital readiness, accessibility needs, self-regulation capacity, and disciplinary context. While these models are helpful, they frequently limit complex teaching and learning environments to simple patterns of user behaviour. It is also clear that voices from teachers, support staff, and less-represented student groups are missing from the discussion. This is considered a serious concern because these technologies are increasingly being used in education. Moving forward, there is a clear need to shift away from rigid, technology-led models toward more adaptive, pedagogy-focused approaches. These findings contribute to a deeper understanding of blended learning as an interconnected educational system. They also offer implications for future research, inclusive digital pedagogy, and policy development in higher education. Full article

The rapid integration of artificial intelligence in higher education positively affects student satisfaction, engagement, and learning outcomes. However, students frequently report ethical unease, guilt, and concerns about dependency. The current literature offers a limited explanation for their coexistence, as both have been treated as parallel or independent outcomes. Hence, this review extends and integrates existing theories by reconceptualising cognitive and moral dissonance as a central psychological process that explains how student satisfaction with AI-mediated learning is produced, negotiated, and sustained. Following PRISMA 2020 guidelines, we adopted a two-layer explanatory review design, synthesising 40 Scopus-indexed studies (Layer 1 = 15 studies; Layer 2 = 25 studies) from 2016 to 2025. Layer 1 studies explicitly define dissonance-related explanatory mechanisms that influence satisfaction and continued AI use across contexts such as dissertation writing, programming education, and problem-based learning. Layer 2 encompasses satisfaction-based studies that report ethical or affective concerns in parallel without theorising their interaction. The findings suggest a recurring satisfaction–dissonance paradox, in which students often experience genuine or conditional satisfaction from performance gains while simultaneously managing their psychological discomfort through one or more regulation mechanisms. Further, persistent and escalated dissonance leads to withdrawal or full or partial adaptive behaviour. We propose these dynamics as a testable Dual-Process Satisfaction–Dissonance Framework (DPSDF), which includes five dissonance triggers, five regulation strategies, three feedback loops, and four behavioural outcomes. Further, five domain experts’ suggestions have been taken to provide specific practical implications. This framework extends understanding of AI-mediated learning and provides foundations for future theory and policy development in higher education. Full article

Dementia is a leading and growing cause of disability worldwide. Insomnia is highly prevalent in mild cognitive impairment (MCI) and dementia and is associated with impaired cognition, functional decline, and reduced quality of life (QoL). Although sleep disturbance represents a potentially modifiable risk factor within the trajectory of cognitive impairment, there have not been many studies conducted to examine the feasibility or preliminary efficacy of digital cognitive behavioural therapy for insomnia (dCBT-I) in this population. The aim of this pilot randomised controlled feasibility study is to evaluate the acceptability, adherence, and potential clinical effects of a multi-component dCBT-I programme (Sleepio) in adults with MCI or mild dementia and comorbid insomnia. Thirty community-dwelling adults aged ≥50 years with established MCI or mild dementia (Mini-Mental State Examination ≥18; Clinical Dementia Rating 0.5–1.0) and insomnia (Sleep Condition Indicator ≤16) will be randomised (1:1) to Sleepio or a wait-list control. Feasibility outcomes include recruitment and retention rates, intervention adherence (completion of ≥4/6 sessions), and acceptability measured using validated usability scales. Secondary outcomes include changes in sleep, mood, QoL, cognition, and function over 10 weeks. Adverse events will be monitored to assess safety. Findings will inform the design of a future definitive trial evaluating digital sleep interventions in cognitively impaired populations. Ethical approval has been granted. The trial is registered at ClinicalTrials.gov (NCT07363928). Full article

The aim of the present study was to explore differences in fair play attitudes among university students in Hungary and Kenya using the Fair Play Attitude Scale (EAF). The questionnaire was culturally adapted for the Kenyan context and administered in both countries. A total of 2090 university students participated in the survey (1278 from Kenya and 812 from Hungary). The scale measures three dimensions of fair play attitudes: gamesmanship and the importance of winning, acceptance of rough play and cheating, and fair play and enjoyment of the game. Principal component analysis confirmed the three-factor structure of the instrument, and reliability indices indicated satisfactory internal consistency in both samples. Due to the non-normal distribution of the variables, non-parametric statistical procedures were applied to examine differences between groups. The results revealed significant cross-cultural differences in fair play attitudes. Kenyan students, particularly men, showed higher acceptance of competition-oriented behaviour and gamesmanship, whereas Hungarian students placed greater emphasis on enjoyment and adherence to fair play principles. The findings highlight the role of cultural and social contexts in shaping ethical attitudes in sport and underline the importance of fair play education in sport pedagogy and educational practice. Full article

This study examines Responsible Decision-Making (RADM) in AI-enabled sustainability within tertiary education under conditions of uncertainty and complex interdependence. Conventional analytical approaches are limited in such settings because they typically explain behavioural relationships without adequately modelling uncertainty. To address this limitation, the study proposes an AI-driven Decision Support System (DSS) based on a hybrid probabilistic framework integrating PLS-SEM with Bayesian Network (BN) inference. The framework combines structural analysis with probabilistic reasoning in a unified, interpretable system capable of modelling conditional dependencies among decision variables. Data were collected from 713 academic leaders in tertiary education institutions in Saudi Arabia. The model examines the effects of AI-Driven Sustainable Value (AISV), Responsible AI Ease of Use (RAIU), Institutional Sustainability Support (ISS), Ethical Leadership Norms (ELN), Responsible AI Competence (RAC), and AI Risk and Hallucination Awareness (ARHA) on Responsible Decision-Making and Sustainability Impact Performance (GGIP). The results indicate that ELN and ARHA have significant positive effects on RADM, while AISV and RAIU also contribute positively to decision quality. In contrast, ISS and RAC do not demonstrate significant direct effects on RADM. However, ISS shows indirect effects through contextual and cognitive pathways. The findings further suggest that awareness of uncertainty and AI-related risks plays a more influential role in decision quality than technical competence alone. The model demonstrates strong explanatory power (R² = 0.64) and acceptable predictive capability (R² = 0.48). Bayesian inference further indicates that sustainability outcomes improve under favourable institutional and cognitive conditions. Overall, the framework provides an interpretable and scalable DSS that supports scenario-based evaluation and probabilistic decision analysis under uncertainty. The findings are specific to the institutional context examined in this study. Although the framework may have relevance to other organisational environments characterised by uncertainty and complex decision structures, no external or cross-contextual validation was conducted. Therefore, the findings should be interpreted with appropriate contextual caution. Full article

Abusive relationships are too often explained solely in terms of individual behaviour, as if a woman’s decision to stay were simply a matter of psychology or poor judgement. In South African communities, however, the reality is considerably more complex. The reasons women remain are situated within what can be described as a distorted process of care: a network of relational, material, and structural forces that alter the very meaning of care itself. This study aimed to explore these interconnections. Guided by an ethics of care framework, we employed multimodal qualitative methods to engage participants from four South African communities between August 2024 and July 2025. Participants (n = 262) were recruited through snowball, purposive, and convenience sampling. Data were coded using ATLAS.ti V8 and analysed thematically. Five interconnected themes shaped the framework. Distorted care described how caregiving could become coercive, shaped by fear, rigid gender roles, intergenerational abuse, and substance misuse. Care under constraint highlighted the material limitations, financial dependency, daily survival challenges, and self-sacrificing caregiving, that left women depleted. The silence of care captured emotional withdrawal, isolation, and the disabling effect of shame on help-seeking. Reclaiming care traced the tentative routes towards healing through ethical self-care, faith, forgiveness, and a conscious effort to disrupt harmful patterns. Woven throughout was structural failure, including absent family networks, the moral decline of communities, and institutional systems that consistently failed women. Remaining in an abusive relationship is not a sign of weakness. It is a negotiation, profoundly constrained, within systems of care that have been fundamentally distorted. Effective intervention should move beyond framing gender-based violence as an individual problem and address it as a collective one, restoring care as a shared social and political responsibility. Full article

As Artificial Intelligence (AI) becomes increasingly integrated into financial services, its alignment with sustainability goals has given rise to a new domain: Green FinTech. This study investigates the Behavioural Intention (BI) of Greek banking customers to adopt AI chatbots in the context of sustainable digital finance. Building upon the Unified Theory of Acceptance and Use of Technology (UTAUT), the proposed model incorporates additional constructs, i.e., Trust, Digital AI Literacy (DAIL), Environmental Concern (ENC), and Consumer Social Responsibility (CnSR), to examine the behavioural intention (BI) to use AI chatbots in the context of sustainable digital finance. Unlike prior UTAUT-based research, which has mainly examined AI, FinTech, or chatbot adoption separately or in different contexts, the present study develops and empirically tests an extended green-oriented UTAUT model that integrates technological, environmental, and ethical dimensions within a single framework. In this way, the study addresses a geographical, contextual, and model-specific gap in the literature, as research on AI chatbot adoption in Green FinTech remains limited, particularly in the Greek banking context. The target population for this study consists of educated, working-age adults who have already used an AI chatbot for a banking transaction in the context of e-banking services. A structured questionnaire was administered to a sample of 209 users of AI chatbots in the banking context. Using Structural Equation Modelling (SEM) and factor analysis via Principal Component Analysis (PCA) in conjunction with orthogonal rotation (VARIMAX), the results show that Green Performance Expectancy (GPE), Green Effort Expectancy (GEE), Digital AI Literacy (DAIL), and Trust significantly influence Behavioural Intention (BI). Consumer Social Responsibility (CnSR) also has an indirect impact via Green Social Influence (GSI). The study extends UTAUT in the Green FinTech context by integrating sustainability- and AI chatbot usage-related constructs, showing that Green Performance Expectancy and trust are the strongest drivers of bank customers’ behavioural intention to use AI chatbots. The study therefore contributes theoretically by extending UTAUT into a green-oriented framework that captures sustainability-related and ethical drivers of AI chatbot adoption in banking, rather than examining technology-use determinants alone. More specifically, it explains AI chatbot adoption in Green FinTech through a unified framework that combines core UTAUT variables with Trust, Digital AI Literacy, Environmental Concern, and Consumer Social Responsibility in the underexplored context of Greek banking. Full article

Background: Nocebo effects are described as adverse symptoms arising from negative expectations rather than direct physiological harm, and are increasingly recognised across healthcare. While traditionally examined within pharmacological trials, emerging literature suggests that nocebo effects are shaped by broader interactional, situational, and communicative processes. In dentistry and paediatric care, where behaviour support and reassurance are central to practice, these mechanisms remain under-synthesised. Objectives: This scoping review aimed to map how nocebo effects are conceptualised across healthcare literature, with particular attention to the role of communication, reassurance, and behaviour support, and to explore how these mechanisms are discussed in paediatric, procedural, and dental contexts. Methods: An interpretive scoping review was conducted in line with JBI guidance and PRISMA-ScR reporting standards. Multidisciplinary literature spanning experimental, clinical, ethical, and applied domains was systematically identified and charted. Studies were grouped using a conceptual framework encompassing expectancy, learning, communication-mediated, ethical, and contextual mechanisms, allowing overlap between categories. Results: A large and heterogeneous body of literature was identified, with most studies conceptualising nocebo effects through overlapping mechanisms rather than discrete pathways. Expectancy and learning processes formed a foundational substrate across contexts, while communication, including framing, tone, reassurance, and checking-in, emerged as an active mechanism shaping symptom perception and vigilance. Ethical discussions highlighted tensions between transparency and potential harm, particularly in consent and risk communication. Paediatric and procedural settings, including dental sedation, were comparatively underrepresented despite features likely to amplify nocebo effects, such as reduced agency and heightened attentional focus. Conclusions: Nocebo effects are best understood as interactional phenomena that emerge within everyday clinical encounters. This review highlights the need to critically examine behaviour support practices, including reassurance, that are typically assumed to be benign. Greater conceptual clarity and reflexivity in communication may support future research and training aimed at minimising unintended distress within dental and paediatric care. These findings suggest that routine communication practices, including reassurance and expectation-setting, should be understood as active components of care that can influence patient experience, rather than as neutral or purely supportive interactions. Full article

Precision Livestock Farming (PLF) has emerged as an approach in modern animal production, integrating advanced technologies such as sensors, automation, data analytics, and artificial intelligence to enable continuous, individualised monitoring of livestock and their environment. This review examines the impact of PLF technologies on three critical dimensions of livestock systems: productivity, animal welfare, and environmental sustainability. PLF applications, including wearable and environmental sensors, automated feeding and milking systems, and video-based monitoring, allow for early detection of health and behavioural deviations, optimisation of feed efficiency, and improved reproductive and disease management. These technologies support proactive, data-driven decision-making that enhances productivity while promoting animal welfare and reducing the environmental footprint of livestock production. Despite these benefits, the adoption of PLF faces significant challenges, including high initial investment costs, technical limitations, system integration issues, data ownership and privacy concerns, and ethical considerations related to automation. Future research and policy efforts should focus on developing cost-effective, scalable solutions, standardised data frameworks, and supportive regulatory measures to enable equitable and responsible implementation across diverse production systems. By addressing these challenges, PLF offers a pathway towards more efficient, welfare-oriented, and environmentally sustainable livestock production, contributing to global food security and resilient agricultural systems. Full article

Background: Standardized and ethically compliant animal models remain essential for improving translational research in Alzheimer’s disease. Although Aβ_1–42-induced rodent models are widely used, methodological variability continues to limit reproducibility. Methods: We explored the feasibility of a stereotactic intrahippocampal Aβ_1–42 rat model established by bilaterally injecting pre-aggregated peptide into the hippocampus of adult Sprague Dawley rats. Model feasibility and targeting accuracy were assessed intraoperatively. Cognitive performance was evaluated using the Y-maze for spatial recognition memory and the novel object recognition (NOR) test. Histological examination was performed using hematoxylin–eosin (H&E) and Congo red staining to assess cytoarchitecture and to provide supportive evidence of amyloid-like deposits. Results: The surgical procedure was well-tolerated, and the injected animals showed reduced performance in behavioural testing, including reduced spatial recognition memory in the Y-maze and decreased discrimination indices in the NOR test. The animals also showed histological changes, including Congo red-positive birefringent structures consistent with amyloid-like congophilic material. Conclusions: This study presents a feasible experimental framework for intrahippocampal Aβ_1–42 administration, showing behavioural and histological changes under the present experimental conditions. However, further validation, including sham-operated controls and molecular characterization, will be required before these findings can be interpreted as specific to Aβ-driven pathology. Full article

Background: Behavioural therapy (BT), including Comprehensive Behavioural Intervention for Tics (CBIT), is an evidence-based first-line treatment for patients with tic disorders. However, access remains limited due to a shortage of trained providers, geographical barriers, costs, and high treatment burden for patients and families. Rapid advances in digital health technologies including telemedicine, web-based treatment platforms, and mobile applications offer new opportunities to expand access to BT for individuals with tic disorders across the lifespan. Methods: For the purpose of this narrative review, we conducted a literature search in PubMed, Europe PMC, and the Cochrane Library to identify relevant studies investigating the effectiveness of digital health treatment approaches in tic disorders. Results: A total of 16 original studies were included. Although the available evidence remains limited and heterogeneous, existing studies suggest that emerging technologies for delivering behavioural interventions for tic disorders, including telehealth-based CBIT, digital therapy platforms, and app-supported habit reversal training (HRT), are feasible, cost-effective, user-friendly, flexible, and safe. These approaches also appear effective for symptom monitoring and personalized treatment support in both pediatric and adult populations. Conclusions: Recent technological advances have the potential to reduce the treatment gap in tic disorders, provided that these approaches are implemented within rigorous, evidence-based, and ethically grounded frameworks. Full article