Search Results (59)

As of 2025, there are approximately 154.3 million voice assistant users in the United States (Emarketer, 2025). Given the prevalence of digital voice assistants in children’s lives, it is critical to understand how children interact with and learn from such digital technologies. Across two experiments, we utilized a modified selective trust design to explore children’s (N = 310) information-seeking behaviors towards technological and human sources in the science domain. In Experiment 1 (N = 143), we asked whether children (aged 4–6) are more likely to direct scientific questions towards and trust in scientific explanations from a digital voice assistant or a peer. The experiment included three parts: (i) scientific ask and endorse phase (ii) explicit judgement phase and (iii) digital voice assistant familiarity question phase. In the first part of the scientific ask and endorse phase, children were asked who they would rather ask to answer certain scientific questions. In the second part of this phase, the digital voice assistant and the peer each provided an explanation in response to that question. Half of the children were assigned to a condition where the digital voice assistant provided a noncircular explanation, and the other half of the children were assigned to a condition where the peer provided a noncircular explanation. In Experiment 2 (N = 167), we examined children’s preference to pose scientific questions to and trust in explanations from a digital voice assistant or a classroom teacher. Across both studies, children preferred to ask questions and trust scientific explanations from the digital voice assistant rather than the peer or the teacher. By understanding how children learn with and through digital technologies in the domain of science, we can design future interventions that leverage conversational AI to further enhance children’s science engagement and critical thinking skills during the early childhood years. Full article

Background: Elderly women face significant health challenges, including knee osteoarthritis (KOA) and balance disorders. Artificial intelligence (AI)-assisted exercise intervention can address limitations of traditional intervention methods, such as low compliance and high economic costs. Objective: This randomized controlled trial (RCT) evaluated the effects of AI-assisted Baduanjin exercise on physical health (balance and knee function) in elderly women, comparing it with offline manual guidance and health education. The group of 79 elderly women (60–74 years) were randomly assigned into three groups: AI-assisted Baduanjin (AI group, n = 25), offline instructor-led Baduanjin (Offline group, n = 27), and health education (Education group, n = 27). Methods: Interventions lasted 12 weeks, with three 45-min sessions per week. Two outcome measures were evaluated pre- and post-interventions: postural stability assessed by the unipedal stance test and knee function measured using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). This study considers two measurement methods. One is a two-way repeated-measures analysis of variance used to evaluate the effects on the three intervention groups. The other is an independent-samples t-test, with post hoc testing (Bonferroni), used to assess differences among the three groups. Results: Both the AI and Offline groups showed significant improvements in WOMAC pain and function scores at 12 weeks (p < 0.05), with the Offline group demonstrating greater functional improvement (decrease in WOMAC function score: 6.7 points, Cohen’s d = 1.23, 95% CI 0.81–1.65). No serious adverse events (e.g., falls, exacerbation of joint pain) were reported in any group. The Offline group also showed immediate balance enhancement (closed-eye stance improvement, effect size d ≈1.57), while the AI group exhibited progressive pain relief. The Education group showed minimal improvements. Inter-group comparisons showed the AI and Offline groups outperformed the Education group in balance and knee function (p < 0.05). Conclusions: AI-assisted and offline Baduanjin interventions effectively improve balance and knee function in elderly women, with offline guidance offering improvement of balance ability. AI intervention is suitable for rural elderly women with low digital literacy, as it provides simplified operation and voice prompts to ensure adherence. Full article

The collection of patient-reported outcome measures (PROMs) is a key measurement tool for patient-centred care. At the same time, collecting these measures poses obstacles for many patients, leading to these groups being underrepresented in the data. We have therefore developed a multimodal, AI-driven assistance system to support patients in collecting these data. The interface of the system comprised a digital tablet containing the PROM questionnaire items and the assistant in three forms of embodiment: A virtual avatar, a physical avatar, and a voice-only agent. To evaluate the users’ experience and ratings of the system, two separate studies were implemented in two rehabilitation centers with 195 patients. A mixed within–between RCT was conducted at an outpatient clinic, where patients completed PROMs both with and without an assistant, and a between-subject design at an inpatient clinic comparing routine PC-based care with avatar- and robot-assisted PROM administration. Our results suggest a preference for the non-assisted tablet-only condition in Clinic A, whereas, in Clinic B, both agent conditions were preferred over routine care. We have further analyzed aspects such as trust and social presence in this study to gain a more thorough understanding of the users’ experience. Our analysis shows a higher trust rating for the voice-only assistant, whereas the robot, virtual avatar, and the voice-only conditions were perceived as more socially present. The impact of demographic factors and affinity for technology on the user ratings was also thoroughly studied. Our findings shed light on the role of agent embodiment in PROM assistance and contribute to the future design and evaluation of effective, engaging, and trustworthy systems for data collection in healthcare settings. Full article

Objectives: To synthesize evidence on artificial intelligence (AI)-enabled medical history taking (anamnesis)—beyond large language models (LLMs) alone—and to translate findings into implications and research priorities for head and neck surgery. Methods: We performed a PRISMA-informed narrative review. Searches from database inception to 31 December 2025 (updated 3 January 2026) were conducted in MEDLINE (PubMed), Embase, Scopus, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library, supplemented by medRxiv/arXiv screening and citation chasing. We included studies evaluating or describing AI-supported history capture/summarization, conversational interviewing, symptom checker/digital triage, EHR-integrated intake-to-decision support pipelines, voice interviewing, education/training systems, and governance/ethical considerations related to digital anamnesis. Findings were synthesized by system category and by cross-cutting outcome domains, with a head and neck surgery interpretive lens. Results: Fifty studies (2014–2025) were included. Evidence most consistently suggested feasibility and acceptability of pre-consultation computer-assisted history taking and the potential to reduce documentation burden and improve structured capture. In contrast, symptom checkers and digital triage tools showed highly variable diagnostic/triage performance and prominent safety concerns, highlighting the importance of conservative red-flag escalation strategies, continuous monitoring, and clear accountability. LLM-based diagnostic dialogue demonstrated strong performance in controlled evaluations, but prospective real-world validation, governance, and workflow integration remain limited. Conclusions: AI-enabled anamnesis comprises heterogeneous tools with uneven evidence. For head and neck surgery, potential near-term applications may include structured pre-visit intake, clinician-facing summarization, and training applications, whereas autonomous triage warrants harm-oriented, specialty-calibrated validation and robust governance prior to broader clinical reliance. Full article

The lack of accessibility in digital gaming platforms remains a significant barrier to equitable user participation. To address this issue, this article presents an inclusive solution developed as a multimedia project designed to promote access to digital games for any user through the ipcb.games platform. The platform offers features that enhance accessibility, including voice-based authentication, voice-assisted registration, facial recognition, visual and auditory feedback, and a simplified interface. It also enables users to submit their own games for subsequent approval and integration. The development process followed a multimedia project methodology, structured into phases of analysis, planning, design, production, testing, and validation. The proposal was informed by a systematic review of scientific literature on digital inclusion and accessibility, complemented by a comparative analysis of existing platforms. During usability testing, the platform was evaluated by approximately 50 teachers from different educational levels, who provided highly positive feedback. Future work includes implementing voice-controlled gameplay, enabling keyboard-based navigation, re-implementing a functional eye-tracking system, and creating pedagogical groups, further strengthening the platform’s role in educational contexts. Full article

Neuromuscular diseases are biologically diverse, clinically heterogeneous, and often difficult to diagnose and treat, highlighting the need for computational tools that can help resolve overlapping phenotypes and support timely, mechanism-informed interventions. This narrative review synthesizes recent advances in artificial intelligence (AI) and machine learning applied to neuromuscular diseases across diagnosis, outcome modeling, biomarker development, and therapeutics. AI-based approaches may assist clinical and genetic diagnosis from phenotypic data; however, early phenotype-driven tools have seen limited clinician adoption due to modest accuracy, usability challenges, and poor workflow integration. Electrophysiological studies remain central to diagnosing neuromuscular diseases, and AI shows promise for accurate classification of electrophysiological signals. Predictive models for disease outcome and progression—particularly in amyotrophic lateral sclerosis—are under active investigation, but most remain at an early stage of development and are not yet ready for routine clinical use. Digital biomarkers derived from imaging, gait, voice, and wearable sensors are emerging, with MRI-based quantification of muscle fat replacement representing the most mature and widely accepted application to date. Efforts to apply AI to therapeutic discovery, including drug repurposing and optimization of gene-based therapies, are ongoing but have thus far yielded limited clinical translation. Persistent barriers to broader adoption include disease rarity, data scarcity, heterogeneous acquisition protocols, inconsistent terminology, limited external validation, insufficient model explainability, and lack of seamless integration into clinical workflows. Addressing these challenges is essential to moving AI tools from the laboratory into clinical practice. We conclude with a practical checklist of considerations intended to guide the development and adoption of AI tools in neuromuscular disease care. Full article

Artificial intelligence (AI) is transforming customer experience management (CXM) by enabling real-time, data-driven, and personalized interactions across digital touchpoints, including chatbots, voice assistants, generative AI, and immersive platforms. This study presents a PRISMA-based systematic literature review of 59 peer-reviewed studies published between 2021 and 2026, examining how AI-enabled personalization, privacy concerns, and customer value interact within AI-mediated customer experiences. Drawing on the Personalization–Privacy–Value (PPV) framework, the review synthesizes evidence on how AI-driven personalization enhances utilitarian, hedonic, experiential, relational, and emotional value, thereby strengthening satisfaction, engagement, loyalty, and behavioral intentions. At the same time, the findings reveal persistent tensions, as privacy concerns, perceived surveillance, algorithmic bias, and contextual moderators—including generational differences, cultural expectations, and technological literacy—frequently constrain value creation and erode trust. The review highlights that personalization benefits are highly contingent on transparency, perceived control, and ethical alignment, rather than personalization intensity alone. The study contributes by integrating ethical AI considerations into CXM research and clarifying conditions under which AI-enabled personalization leads to value creation versus value destruction. Managerially, the findings underscore the importance of ethical governance, transparent data practices, and customer-centered AI design to sustain trust and long-term customer relationships. Future research should prioritize longitudinal analyses of trust development, demographic heterogeneity, and cross-sector comparisons of AI governance as AI technologies become increasingly embedded in service ecosystems. Full article

Smart virtual reality (VR) systems are becoming central to media production education, where immersive practice, real-time feedback, and hands-on simulation are essential. This review synthesizes the integration of artificial intelligence (AI) into human-centered, interactive VR learning for television and media production. Searches in Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and SpringerLink (2013–2024) identified 790 records; following PRISMA screening, 94 studies met the inclusion criteria and were synthesized using a systematic scoping review approach. Across this corpus, common AI components include learner modeling, adaptive task sequencing (e.g., RL-based orchestration), affect sensing (vision, speech, and biosignals), multimodal interaction (gesture, gaze, voice, haptics), and growing use of LLM/NLP assistants. Reported benefits span personalized learning trajectories, high-fidelity simulation of studio workflows, and more responsive feedback loops that support creative, technical, and cognitive competencies. Evaluation typically covers usability and presence, workload and affect, collaboration, and scenario-based learning outcomes, leveraging interaction logs, eye tracking, and biofeedback. Persistent challenges include latency and synchronization under multimodal sensing, data governance and privacy for biometric/affective signals, limited transparency/interpretability of AI feedback, and heterogeneous evaluation protocols that impede cross-system comparison. We highlight essential human-centered design principles—teacher-in-the-loop orchestration, timely and explainable feedback, and ethical data governance—and outline a research agenda to support standardized evaluation and scalable adoption of smart VR education in the creative industries. Full article

Virtual assistants in academic environments often lack comprehensive multimodal integration and specialized role-based architecture. This paper presents NAIA (Nimble Artificial Intelligence Assistant), a robust artificial intelligence framework designed for multi-role virtual academic assistance through a modular monolithic approach. The system integrates Large Language Models (LLMs), Computer Vision, voice processing, and animated digital avatars within five specialized roles: researcher, receptionist, personal skills trainer, personal assistant, and university guide. NAIA’s architecture implements simultaneous voice, vision, and text processing through a three-model LLM system for optimized response quality, Redis-based conversation state management for context-aware interactions, and strategic third-party service integration with OpenAI, Backblaze B2, and SerpAPI. The framework seamlessly connects with the institutional ecosystem through Microsoft Graph API integration, while the frontend delivers immersive experiences via 3D avatar rendering using Ready Player Me and Mixamo. System effectiveness is evaluated through a comprehensive mixed-methods approach involving 30 participants from Universidad del Norte, employing Technology Acceptance Model (TAM2/TAM3) constructs and System Usability Scale (SUS) assessments. Results demonstrate strong user acceptance: 93.3% consider NAIA useful overall, 93.3% find it easy to use and learn, 100% intend to continue using and recommend it, and 90% report confident independent operation. Qualitative analysis reveals high satisfaction with role specialization, intuitive interface design, and institutional integration. The comparative analysis positions NAIA’s distinctive contributions through its synthesis of institutional knowledge integration with enhanced multimodal capabilities and specialized role architecture, establishing a comprehensive framework for intelligent human-AI interaction in modern educational environments. Full article

Managing blood glucose levels and adhering to exercise is challenging for older adults with obesity and type 2 diabetes mellitus (T2DM). Digital voice assistants (DVAs) utilising conversation-based interactions and natural language may overcome barriers to accessing home-based lifestyle programs, but end-user perspectives are essential for implementation. This analysis investigated end-user perspectives on implementation outcomes of a DVA-delivered lifestyle program nested within a randomised controlled trial of 50 older adults (aged 50–75 years) with obesity and T2DM (DVA n = 25; control n = 25). Following trial completion, 10 DVA participants (mean ± SD age 67 ± 4 years) completed semi-structured interviews guided by the Practical Planning for Implementations and Scale-up guide and Proctor’s implementation outcome taxonomy. Over half (60%) were willing to pay for the DVA-delivered program, indicating perceived value. DVA audiovisual and conversation-based modalities enhanced engagement and acceptability. Most end-users found the DVA program feasible as a modality for delivering lifestyle programs, but suggested greater personalisation to bolster sustainability. Overall, the intervention was identified as acceptable and appropriate, suggesting digitally delivered programs may be feasible and sustainable for long-term use. Findings should be interpreted cautiously, given the small sample size and short intervention period. Nevertheless, end-users’ suggestions could inform the implementation of digital health interventions into healthcare systems. Full article

Background: Rapid population aging poses significant challenges to health and wellness systems, necessitating innovative technological interventions. Smart home technologies, particularly voice-activated intelligent assistants (smart speakers), represent a promising avenue for supporting aging populations. Objectives: This study critically examines the empirical literature on smart speakers’ influence on older adults’ health and well-being, mapping the characteristics of existing studies, assessing the current state of this domain, and providing a comprehensive overview. Methods: A mixed-methods systematic review was conducted in accordance with published guidelines. Bibliometric data, article purposes and outcomes, keyword network analysis, and mixed-methods findings from articles retrieved from five major databases were managed through the Covidence and VosViewer applications. Results: The majority of studies were conducted in the American region. Bibliometric analysis revealed five predominant thematic clusters: health management, psychological support, social connectedness, technology adoption, and usability. Findings demonstrated multifaceted benefits across several domains. Older adults reported improvements in daily living activities, enhanced emotional well-being, strengthened social connections, and overall health benefits. Qualitative evidence particularly emphasized the advantages of medication adherence, routine maintenance, and facilitated social support. However, mixed-method synthesis revealed significant barriers to adoption and sustained use, including privacy concerns, technical difficulties, cost constraints, and limited digital literacy among older users. Conclusions: The integration of smart speakers into the homes of older adults offers considerable potential to enhance technological wellness and promote successful aging in place, underscoring the need for structured integration of smart speaker technology and human-centered designs within geriatric care systems. Full article

This paper outlines the process of developing a low-cost system for home appliance control via real-time hand gesture classification using Computer Vision and a custom lightweight machine learning model. This system strives to enable those with speech or hearing disabilities to interface with smart home devices in real time using hand gestures, such as is possible with voice-activated ‘smart assistants’ currently available. The system runs on a Raspberry Pi 5 to enable future IoT integration and reduce costs. The system also uses the official camera module v2 and 7-inch touchscreen. Frame preprocessing uses MediaPipe to assign hand coordinates, and NumPy tools to normalise them. A machine learning model then predicts the gesture. The model, a feed-forward network consisting of five fully connected layers, was built using Keras 3 and compiled with TensorFlow Lite. Training data utilised the HaGRIDv2 dataset, modified to consist of 15 one-handed gestures from its original of 23 one- and two-handed gestures. When used to train the model, validation metrics of 0.90 accuracy and 0.31 loss were returned. The system can control both analogue and digital hardware via GPIO pins and, when recognising a gesture, averages 20.4 frames per second with no observable delay. Full article

The rapid proliferation of generative artificial intelligence (GenAI) in educational settings has created unprecedented opportunities for language instruction, yet empirical evidence regarding its efficacy in primary-level English as a Foreign Language contexts remains scarce, particularly concerning pre-service teachers’ implementation experiences during formative practicum periods. This investigation, conducted in a public school in a non-Anglophone country during the Spring of 2025, examined the impact of GenAI-driven gamified activities on elementary pupils’ English language competencies while exploring novice educators’ professional development trajectories through a mixed-methods quasi-experimental approach with comparison groups. Four third-grade classes (n = 119 individuals aged 8–9) in a public school were assigned to either ChatGPT-mediated voice-interaction games (n = 58) or conventional non-digital activities (n = 61) across six 45 min lessons spanning three weeks, with four female student-teachers serving as instructors during their culminating practicum. Quantitative assessments of grammar, listening comprehension, and pronunciation occurred at baseline, post-intervention, and one-month follow-up intervals, while reflective journals captured instructors’ evolving perceptions. Linear mixed-effects modeling revealed differential outcomes across linguistic domains: pronunciation demonstrated substantial advantages for GenAI-assisted learners at both immediate and delayed assessments, listening comprehension showed moderate benefits with superior overall performance in the experimental condition, while grammar improvements remained statistically equivalent between groups. Thematic analysis uncovered pre-service teachers’ progression from technical preoccupations toward sophisticated pedagogical reconceptualization, identifying connectivity challenges and assessment complexities as primary barriers alongside reduced performance anxiety and individualized pacing as key facilitators. These findings suggest selective efficacy of GenAI across language skills while highlighting the transformative potential and implementation challenges inherent in technology-enhanced elementary language education. Full article

McMahon Hall, an iconic Brutalist dormitory at the University of Washington, has become the site of an interdisciplinary experiment in cultural memory and machine-assisted storytelling. This article presents a method that combines remote sensing with AI-generated voices to produce a polyvocal narrative of architecture through the perspective of the building itself, its material (concrete), an architect, a journalist, and a bird. Drone photogrammetry and generated 3D models were combined with generative AI (text, image, and voice) to reconstruct the site digitally and imaginatively (AI-driven speculative narratives). Through speculative storytelling, the article and the project explore how cultural memory and perception of built heritage can be augmented by machines, offering plural perspectives that challenge singular historical narratives. The Introduction situates the work at the intersection of digital heritage documentation, AI storytelling, epistemology in machine learning, and spatial computing, emphasizing the perception of heritage through different actors. The Theoretical Framework draws on literature in photogrammetry for heritage preservation, polyvocal narrative, and knowledge frameworks of AI. The Materials and Methods detail the workflow: capturing McMahon Hall via UAV photogrammetry, producing a 3D model, and generating character-driven narratives with large language models and voice synthesis. The resulting multi-voiced narrative and its thematic insights are described. In the Discussion, the implications of this approach for architectural heritage interpretation are considered, including its capacity to amplify diverse voices and the risks of bias or hyperreality in AI-generated narratives. The study argues that this polyvocal, machine-augmented storytelling expands the toolkit of remote sensing and digital heritage by not only documenting the tangible form of the built environment but also speculating on its intangible cultural memory. The Conclusions reflect on how merging spatial computing techniques with AI narratives can support new modes of engagement with architecture, positioning this work as a building block toward richer human-machine co-created heritage experiences. Full article

The construction industry, traditionally lagging behind in terms of digitalization, faces significant challenges in its transition to Industry 4.0, which is characterized by the use of advanced technologies such as artificial intelligence (AI), the Industrial Internet of Things (IIoT), and cloud computing. This article presents the development and implementation of an AI-based virtual assistant, designed to optimize the operation and maintenance of concrete production plants. The assistant helps reduce the margin of human error, improve operational efficiency, and facilitate continuous training for operators. These advancements foster a more collaborative and digitalized environment, while also generating environmental, economic, and social benefits: reduced material and energy waste, lower carbon footprint, increased workplace safety, and strengthened organizational resilience. The results show high accuracy in voice transcription (96%) and a 100% success rate in responding to technical queries, demonstrating its effectiveness as a support tool in industrial settings. Based on these findings, it is concluded that the incorporation of AI-based virtual assistants promotes a more sustainable and responsible production model, aligned with the Sustainable Development Goals of the 2030 Agenda, and anticipates the principles of Industry 5.0 by promoting symbiotic collaboration between humans and technology. This innovation represents a key advancement in transforming the concrete industry, contributing to productivity, environmental sustainability, and workplace well-being in the sector. Full article