Search Results (51)

Accessing and retrieving specific information from complex Building Information Modeling (BIM) data, particularly within the Industry Foundation Classes (IFC) schema, remains a challenge for Architecture, Engineering, and Construction (AEC) professionals. Existing methods, including manual parsing, database conversion, and early-stage AI applications, often suffer from high overhead, inflexibility, or reliance on pre-processed, static data views. This study introduces a novel AI agentic framework that leverages a Large Language Model (LLM) to dynamically generate executable code for BIM data query. The core of this framework is a Retrieval-Augmented Generation (RAG) pipeline that, instead of retrieving factual data, retrieves relevant code exemplars demonstrating the use of IFC parser. The LLM-based agent analyzes a user’s natural language query, retrieves functionally relevant code snippets from a specialized vector database, and synthesizes these examples into a tailored, executable script. This “BIM code-retrieval” approach eliminates the need for IFC conversion using databases, allowing the agent to directly use the full power of the IFC parser to obtain the data it needs, and for the LLM to make appropriate inferences to the question. The proposed system demonstrates flexibility and query power compared to methods reliant on static data abstractions. It can handle complex queries that are intractable for previous RAG-based approaches. This AI agent code generation framework demonstrates the cost-effective, inference-based human–BIM interaction method in situations where IFC-to-database conversion is difficult. The prototype of study was developed to analyze the performance of the proposed BIM AI Agent framework and its strengths and weaknesses were examined. Full article

As Artificial Intelligence (AI) evolves from passive tools into proactive actors within socio-technical systems, traditional social network theories face fundamental limitations in explaining AI’s structural power. Drawing on the Network Capabilities framework, this study investigates the mechanism of AI power generation within homogeneous communities from a structural hole perspective. This study analyzes a COVID-19 vaccine interaction network (N = 9314) on X via social network analysis, Propensity Score Matching (PSM), counterfactual simulations, and weighted Independent Cascade Model (ICM) dynamics. The results reveal that bot-like agents do not rely on traditional brokerage to acquire power; instead, they execute a Tight Integration strategy by filling micro-structural holes. After isolating the confounding effects of connection scale via rigorous Propensity Score Matching, it creates an anomalous high-density, high-constraint configuration, with these algorithmic agents exhibiting significantly higher network constraint (0.514) than comparable human users (0.453). Counterfactual removal experiments demonstrate a profound structural dependence of the social system on AI: their removal triggers a systemic cascade collapse, decreasing the largest connected component (LCC) size by a factor of 82.9 and topologically isolating 79.7% of human users. Furthermore, transitioning from static structural analysis to dynamic simulations, ICM simulations confirm AI’s topological redundancy translates into substantial information diffusion dominance (Cohen’s d = 1.081). Revealing AI’s power generation mechanism provides essential governance insights and strategic approaches for mitigating AI-driven information cocoons and group polarization. Full article

Proficiency in history-taking is foundational to safe clinical practice, yet traditional teaching methods offer infrequent opportunities for deliberate practice. This study evaluated whether an AI-powered virtual patient chatbot with a custom knowledge base could improve history-taking competence among fifth-year medical students during general medicine rotations. A mixed-method, quasi-experimental, non-equivalent control group design was used. The experimental group (N ≈ 157) engaged with four custom knowledge-based chatbot scenarios, while a control cohort (N ≈ 156) followed traditional training within the same curriculum. Assessment was performed using a validated Objective Structured Clinical Examination across two standardized cases, and user perceptions were gathered using an adapted Technology Acceptance Model survey from 105 students, with qualitative feedback from 32 trained students in a focus group. Compared to the control group, the experimental group demonstrated significant improvements in direct assessments (79.2% vs. 74.8%, p = 0.002) and indirect assessments (81.2% vs. 79%, p = 0.026). Participants reported a high user-perception score (80%) across usefulness, ease, attitude, and behavior. Focus group findings highlighted the chatbot’s value as a safe, flexible, feedback-rich tool. The chatbot-based training improved students’ performance and satisfaction and should serve as a supplementary aid rather than a substitute for real patient interactions. Full article

Conventional web analytics treats the human user as its fundamental unit of analysis, assuming stable preferences, identifiable intentions, and behavioral patterns that unfold over time. That assumption is under strain. Crawlers and traditional bots already account for a substantial fraction of online interactions, and autonomous AI agents are emerging as a further class of actors layered on top of this automated traffic. Unlike either, these agents do not possess persistent identities or psychologically grounded motivations. They are task-specific, dynamically instantiated processes whose behaviors are contingent and often orchestrated by external systems. Their presence weakens the interpretive value of core metrics, including sessions, engagement, conversion, and retention. A click may reflect an optimization routine, a proxy objective, or a recursive agent-to-agent exchange rather than meaningful human intent, and traditional inference frameworks cannot reliably distinguish among these possibilities. This is a position paper. It synthesizes literature across bot and agent detection, agent architecture, web measurement validity, governance of automated systems in adjacent sectors, and the epistemology of digital trace data, and it argues that web analytics should supplement, and in places replace, its human-centered model with an agent-aware model focused on interaction dynamics within hybrid ecosystems of human and non-human actors. The paper develops a working taxonomy of crawlers, traditional bots, AI agents, LLM-powered agents, and autonomous agents; identifies three properties of LLM agents (identity discontinuity by design, task-based instantiation, agent-to-agent loops) that distinguish the present challenge from prior bot-detection problems; examines opaque agent objectives, synthetic traffic loops, and the indistinguishability between human-originated and agent-mediated signals; and proposes five candidate measurement primitives (task chain, actor class, interaction provenance, objective alignment, signal authenticity) with explicit operational definitions. Governance machinery from energy systems and critical infrastructure offers a partial template, and we delimit which dimensions transfer and which do not. The contribution is conceptual and programmatic, presenting a vocabulary, set of candidate primitives, and research agenda for a field whose foundational unit of analysis is becoming unreliable. Full article

Human–computer interaction (HCI) has evolved from traditional command-based interfaces to adaptive systems powered by artificial intelligence (AI). In industrial environments, particularly manufacturing and logistics, selecting the appropriate interaction modality is crucial for efficiency, safety, and user acceptance. This study presents a conceptual decision support framework that analyzes three modalities—visual, voice, and multimodal—based on a systematic literature review covering the period from 2003 to early 2026. The analysis evaluates differences in usability, cognitive workload, implementation complexity, and operational benefits of HCI and AI-based HCI. To address the selection challenge, a multi-criteria decision analysis (MCDA) model was developed. The proposed MCDA model is based on a structured literature analysis and expert-informed evaluation. The expert-based MCDA ranking is context-dependent and grounded in the reviewed literature. The results indicate that multimodal HCI shows the highest potential in manufacturing scenarios, offering advantages in safety, robustness, flexibility, and potential contributions to sustainability. However, it also indicates more demanding implementation, training requirements, and higher costs. The proposed decision support framework is intended to serve as a methodological tool for the structured evaluation of HCI modality suitability in sustainable manufacturing environments. Full article

Scalable production of interactive 3D assets is a key requirement for XR-based applications, yet the functional integration of GenAI-generated assets into game engines remains challenging for non-expert users. This article proposes and validates a Prompt-to-Trigger workflow that links GenAI-based asset ideation and generation with the implementation of basic interactive behaviors (triggers) in accessible XR platforms. The study adopted a qualitative and exploratory approach, using systematic observation throughout a two-stage development process. This process included an initial phase where 3D assets were generated and refined using tools such as Tripo AI and Meshy, followed by an optimization stage to ensure compatibility with Blender and XR environments like A-Frame and Godot, and subsequently, the creation of AI-powered activation scripts. The results show that GenAI’s current 3D outputs frequently exhibit topological inconsistencies and rigging errors that compromise performance and real-time interoperability, requiring cleanup and optimization before deployment. The Prompt-to-Trigger workflow formalizes this bridge, positioning AI assistance as a functional layer for iterative logic generation. The resulting model provides non-expert creators with structured, actionable framework to prototype complex XR experiences for applied domains like education and multimedia communication. Full article

The advancement in artificial intelligence (AI)-powered automation has accelerated the integration of AI-powered chatbots into our daily routines, opening novel channels for dynamic information flow and participatory dialogue. Whilst prior studies have examined chatbot interactivity and related outcomes, the mechanism through which information interactivity is translated into relational and advocacy outcomes remains insufficiently theorized, and its conceptual demarcation from active involvement remains underdeveloped. Grounded in Uses and Gratifications (U&G) theory, this study develops and tests a process model of AI-powered chatbot use. In this model, information interactivity is treated as an AI-powered communicative affordance, perceived value represents the mechanism through which gratifications are realized, and active involvement is conceptualized as a situational psychological state that influences customer trustiness and positive word-of-mouth (WOM). Using structural equation modeling on survey data from 588 AI-powered chatbot users, the study finds that information interactivity positively predicts functional, psychosocial, and hedonic value, all of which significantly enhance active involvement. Active involvement, in turn, exerts a significant positive effect on customer trustiness, and customer trustiness significantly promotes positive WOM. By contrast, the direct effect of active involvement on positive WOM is not significant, suggesting that trustiness functions as the more proximal mechanism through which involvement is translated into advocacy. These findings contribute to research grounded in U&G theory by demonstrating how functional, psychosocial, and hedonic value link chatbot interactivity to relational and advocacy outcomes. They also suggest several practical considerations for the development of chatbot services that are more responsive to users’ expectations and trustiness formation. Full article

Given the global aging of the population and the rising retirement age, the development of cross-generational technologies is crucial for a sustainable workforce supply. While AI-powered smart glasses can provide continuous cognitive support, current industrial solutions often prioritize work efficiency at the expense of the physical, cognitive, and socio-emotional needs of older workers. This study employed a mixed-methods approach grounded in embodied cognition. First, semi-structured interviews with ten participants were analyzed using grounded theory to develop a four-dimensional model of embodied experience: Perceived Pressure, Action Feedback, Collaboration Embedding, and Belonging. Subsequently, four interaction strategies—Rhythm Control, Transparent Feedback, Non-intrusive Assistance, and Legible Privacy & Social Signaling—were formulated and implemented. A high-fidelity prototype was developed to embody these strategies. Finally, a team of eight multidisciplinary experts evaluated the device using the System Usability Scale (SUS) and a proprietary twelve-item questionnaire. The results showed that the device’s overall usability was borderline acceptable (SUS = 68.13 ± 8.94). While the devices received stronger ratings for Control & Safety, the ratings for dignity and social acceptance were comparatively low. These findings contribute to the development of wearable device operation strategies suitable for users of different generations, and underline the importance of social and emotional compatibility as a prerequisite for future practice tests. Full article

Background: In recent years, virtual consultations have emerged as a crucial approach for continuity of chronic care provision, indicating a promising avenue for the future of smart healthcare systems. However, reversions to in-person care highlight persistent limitations, despite notable advantages of remote modalities. In parallel, recent developments in artificial intelligence (AI) indicate the potential to enhance remote chronic care, but user perceptions of such assistance and the corresponding human factors remain underexplored. Objective: This mixed methods study aims to better understand the virtual consultation experiences and attitudes toward AI-assisted tools in remote care among patients with noncommunicable chronic conditions and their healthcare professionals (HCPs). It conducts an in-depth examination of the associated human–computer interaction and usability elements of virtual consultations and of potential AI assistance. Methods: Public and Patient Involvement was integrated to run pilots and refine documentations. Semi-structured interviews with patients (n = 10), focus groups with HCPs (n = 15), and an online survey (n = 83) were conducted. Qualitative data was analysed through a reflexive thematic approach. The survey comprised the Telehealth Usability Questionnaire (TUQ) and bespoke items on user AI views, and the data was used to triangulate the qualitative findings. Nonparametric Kruskal–Wallis tests and ε² effect sizes compared TUQ and AI views scores between current and former virtual consultation user groups. Results: Seven themes emerged from the qualitative data, which were supported by the quantitative findings. The statistical analyses resulted in a mean TUQ total score of 90.6 (SD = 15.0), which indicates high usability and user satisfaction; however, they failed to detect a difference between groups (p > 0.05; ε² = 0.002–0.032). There was a clear preference for hybrid models, while a lack of empathy was identified during remote interactions. While a notable proportion of users indicated a literacy gap towards AI use in healthcare settings, they expressed cautious openness towards AI assistance, contingent upon transparency, human oversight, and data integrity; indicating a potential gap between competence to judge the technology and willingness to use it. Significant differences in views on AI assistance across groups failed to be detected (p > 0.05; ε² = 0.005–0.065). Conclusions: Virtual consultations for chronic conditions are widely usable and acceptable, particularly through hybrid approaches. Addressing empathic engagement, holistic patient status, and transparent AI integration can enhance clinical quality and user experiences during remote interactions. However, the low statistical power and failure to detect a difference between groups (likely due to the small sample size) indicate the need for caution when interpreting the quantitative findings. There is also the implicit need to address potential AI literacy gap among users, indicating the need for robust safeguard measures. This study has also identified evidence-based assistive AI features that can potentially enhance virtual consultations. These insights can inform the co-design of evidence-based virtual care platforms, policies and supportive AI tools to sustain remote chronic care delivery. Full article

This study examines how the design and interaction features of AI-powered fintech chatbots shape the customer experience of Generation Z users by integrating the Stimulus-Organism-Response framework with dual-process perspectives. Two cross-sectional surveys were conducted in Türkiye. Study 1 (n = 166) examines the effect of social presence, interactivity, visual appeal, design originality, and usability on perceived competence and perceived warmth, which, in turn, shape the customer experience. Social presence and design originality significantly increased perceived competence (β = 0.47, p < 0.001), while visual appeal enhanced perceived warmth (β = 0.32, p < 0.001). Together, competence and warmth explained a substantial proportion of customer experience (R² ≈ 0.60). Usability and interactivity showed no significant effects. Study 2 (n = 195) replicated these findings with trained users and introduced task complexity as a moderator. Under high task complexity, usability and interactivity became significant predictors of competence, which emerged as the primary driver of customer experience, whereas the influence of warmth diminished. Non-normal data distributions justified the use of Partial Least Squares Structural Equation Modeling. Overall, the findings suggest a shift from heuristic to systematic processing as fintech tasks become more complex, highlighting the growing importance of competence-based evaluations in fintech chatbot interactions. Full article

Conversational agents are transforming digital interactions across various domains, including healthcare, education, and customer service, thanks to advances in large language models (LLMs). As these systems become more autonomous and ubiquitous, understanding what constitutes high-quality interaction from a user perspective is increasingly critical. Despite growing empirical research, the field lacks a unified framework for defining, measuring, and designing user-perceived interaction quality in human–artificial intelligence (AI) dialogue. Here, we present an integrative review of 125 empirical studies published between 2017 and 2025, spanning text-, voice-, and LLM-powered systems. Our synthesis identifies three consistent layers of user judgment: a pragmatic core (usability, task effectiveness, and conversational competence), a social–affective layer (social presence, warmth, and synchronicity), and an accountability and inclusion layer (transparency, accessibility, and fairness). These insights are formalised into a four-layer interpretive framework—Capacity, Alignment, Levers, and Outcomes—operationalised via a Capacity × Alignment matrix that maps distinct success and failure regimes. It also identifies design levers such as anthropomorphism, role framing, and onboarding strategies. The framework consolidates constructs, positions inclusion and accountability as central to quality, and offers actionable guidance for evaluation and design. This research redefines interaction quality as a dialogic construct, shifting the focus from system performance to co-orchestrated, user-centred dialogue quality. Full article

Small-scale farms produce more than one-third of the world’s food supply, making them a crucial contributor to global food security. In this study, an artificial intelligence of things (AIoT) framework is introduced for smart small-scale farm management. For plant disease detection, the lightweight MobileViT model, which integrates vision transformer and convolutional modules, was utilized to efficiently capture both global and local image features. Data augmentation and transfer learning were employed to enhance the model’s overall performance. MobileViT resulted in a test accuracy of 99.5%, with per-class precision, recall, and f1-score ranging between 0.92 and 1.00 considering the benchmark Plant Village dataset (14 species–38 classes). MobileViT was shown to outperform several standard deep convolutional networks, including MobileNet, ResNet and Inception, by 2–12%. Additionally, an LLM-powered interactive chatbot was integrated to provide farmers with instant plant care suggestions. For plant environment management, the powerful, cost-effective ESP32 microcontroller was utilized as the core processing unit responsible for collecting sensor data (e.g., soil moisture), controlling actuators (e.g., water pump for irrigation), and maintaining connectivity with Google Firebase Cloud. Finally, a mobile application was developed to integrate the AI and IoT system capabilities, hence providing users with a reliable platform for smart plant disease detection and environment management. Each system component was each tested individually, before being incorporated into the mobile application and tested in real-world scenarios. The presented AIoT-based solution has the potential to enhance crop productivity within small-scale farms while promoting sustainable farming practices and efficient resource management. Full article

Background/Objectives: The digitization of care opens up new opportunities to support family caregivers, who play a key role in home care. While telemedicine applications have already shown initial relief effects, AI-supported chatbots are increasingly coming into focus as an innovative form of digital support. The aim of this study was to build on an earlier study on the integration of telemedicine into home care and to conduct a complementary study on AI-based chatbots to analyze their acceptance, perceived benefits, and potential barriers from the perspective of family caregivers. Methods: The study comprises two consecutive online surveys with a total of n = 62 family caregivers. The first study assessed the use and acceptance of telemedicine systems; the second complementary survey examined attitudes toward AI-supported chatbots. Both questionnaires were developed based on a systematic literature review and in accordance with the Technology Acceptance Model (TAM). The dimensions of user-friendliness, data protection, communication support, emotional relief, and training needs were among those recorded. The data were evaluated using descriptive statistics, including comparative analyses between the two studies. Results: The results show that family caregivers generally have a positive attitude toward digital health solutions, but at the same time identify specific barriers. While technical barriers and privacy concerns dominated the telemedicine study, the AI results place greater emphasis on psychosocial factors. It also became clear that participants assumed that chatbots would be more acceptable if they were designed to be empathetic and dialogue-oriented. A comparison of the two data sets shows that the perceived benefits of digital systems are shifting from functional support to interactive, emotional support. Conclusions: The results suggest that AI-powered chatbots could offer significant added value to family caregivers by combining information sharing, emotional support, and self-reflection. In doing so, they expand the focus of traditional telemedicine to include a communicative and psychosocial dimension. Future research should examine the actual user experience and effectiveness of such systems in longitudinal and qualitative designs. Despite limitations in terms of sample representativeness and hypothetical usage estimates, the study makes an important contribution to the further development of digital care concepts and the ethically responsible integration of AI into home care. Full article

The rapid evolution of Generative Artificial Intelligence (GenAI) is reshaping the energy sector, enabling new levels of adaptability, efficiency, and user-centric interaction. This review systematically maps and critically evaluates the chosen literature across buildings, grids, and urban systems. Through major scientific databases and for the span of five years, from 2021 to 2025, the review aims to identify key application domains, synergies, and research gaps. The analysis on recent advancements illustrates how GenAI enhances energy forecasting, demand–response strategies, anomaly detection, and cyber-resilience in power networks, while also supporting predictive modeling and optimal control in distributed renewable integration. Within smart buildings, GenAI empowers autonomous agents and AI copilots to balance comfort with energy efficiency through adaptive environmental control and user preference modeling. At the grid level, generative models improve renewable generation forecasting, grid stability, and decision support for operators. A further emerging application lies in the generation of synthetic energy data, which supports model training, scenario simulation, and robust decision-making in data-scarce environments. In the broader context of smart cities, GenAI-driven digital twins, multi-agent systems, and conversational interfaces facilitate sustainable planning and energy-aware citizen engagement. A central theme across these applications is the alignment of technological solutions with human needs and sustainability objectives. Key challenges remain in uncertainty quantification, trustworthy deployment, and data governance, underscoring the need for secure, adaptive, and human-centered GenAI systems to drive the next generation of intelligent energy management. This review provides a comprehensive analysis to promote a better understanding of generative models as they are being applied in a variety of scenarios in the energy domain. Full article

This study used Q methodology to explore and categorise caregivers’ subjective perceptions of artificial intelligence (AI)-powered ‘virtual human’ (AVH) devices in caring for older adults. We derived 123 initial statements from literature and focus groups and narrowed them to 34 statements as the final Q sample. Seventeen caregivers, nurses, and social workers completed the Q-sorting procedure. Using principal component analysis and Varimax rotation in Ken-Q, we identified three perception types: Active Acceptors, who emphasise the devices’ practical utility in patient communication; Improvement Seekers, who conditionally accept the technology while seeking greater accuracy and effectiveness; and Emotional Support Seekers, who view the device as a tool for emotional relief and psychological support. These findings suggest that technology acceptance in caregiving extends beyond functional utility. It also involves trust, affective experience, and interpersonal interaction. This study integrates multiple frameworks, including the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), Science and Technology Studies (STS), and Human–Machine Communication (HMC) theory, to provide a multifaceted understanding of caregivers’ acceptance of AI technology. The results offer valuable implications for designing user-centred AI care devices and enhanced emotional and communicative functions. Full article