Search Results (140)

This study evaluates the service quality of smart campus dining services as a core element of sustainable school meal governance and health-promoting campus environments. A structured questionnaire grounded in the five SERVQUAL dimensions—tangibles, reliability, responsiveness, assurance, and empathy—was administered to 375 users of a smart campus catering platform, including students, faculty and staff, and education administrators from 20 counties and cities in Taiwan. The data were analyzed using gap analysis, confirmatory factor analysis, multiple regression, and Importance–Performance Analysis (IPA) to identify major service quality gaps and sustainability-oriented improvement priorities. The results show that tangibles, reliability, responsiveness, and assurance significantly predict overall service quality, with assurance exerting the strongest effect, while empathy is highly correlated with the other dimensions. IPA further indicates that outdated or insufficient smart facilities fall into the high-importance/low-performance area and thus represent a critical weakness. These findings provide empirical evidence for data-driven and user-centered management of school meal services, supporting more efficient resource allocation, AI-assisted menu planning, and IoT-based food safety monitoring. By linking service quality assessment with sustainable campus governance, the study contributes to efforts to promote healthy eating, reduce food waste, and strengthen localized food supply collaboration, in line with Sustainable Development Goals related to health, education, and responsible consumption. Full article

Electric scooters and other micromobility innovations are becoming standard fare in urban transportation networks. Yet there are several obstacles that must be overcome, including concerns about users’ satisfaction and safety. This study aimed primarily at developing a user-centered methodological framework that combined different user-centered engineering tools such as voice of customers analysis, needs–metrics mapping, Pugh’s matrix and morphological design, strategic analysis approaches such as SWOT and PESTEL, and, a key innovation, the smart terrain-adaptive power management system (STAPMS), an AI-based feature that dynamically adjusts power output and regenerative braking based on Budapest’s varied topography and road conditions to improve energy efficiency and ride comfort. This innovative framework offers insights into redesign options aimed at enhancing customer satisfaction, product quality, and business growth. The proposed framework was validated on Lime electric scooters, particularly the S2 generation type. Three design concepts were generated and evaluated through a systematic approach to provide an optimal balance between users’ needs, technical performance, and strategic feasibility. The proposed user-centered framework shows significant potential to improve users’ satisfaction, enhanced usability, extended range, and increased market competitiveness, validating its viability for micromobility innovative solutions. The findings also demonstrate the necessity for systematic frameworks that link user experience with engineering design and can be generalized to other micromobility products. Full article

In early-stage product ideation, generative AI has emerged as a design support tool, yet most prompts still rely on designers’ subjective intent with limited grounding in user context. Therefore, we developed a user-centered prompting method, introducing a context-based prompt derived from field data, alongside narrative-based and keyword-based prompts as controls. Using these prompts, 15 conceptual images of farming tools for elderly users were generated via DALL·E 3 and evaluated by seven seasoned experts across five criteria. The results show that context-based prompts outperformed others, especially in practicality and intent alignment. A thematic analysis of qualitative feedback further reveals that user-grounded prompts enhance design resonance and usability. This workflow offers implications for improving human–AI co-creation, especially in socially sensitive design contexts. Full article

While creative Artificial Intelligence (AI) tools offer unprecedented creative power, their outputs often create a “personalization gap” by converging towards a generalized “average aesthetic” that ignores nuanced user preferences. This study addresses this challenge with a proof-of-concept computational framework to model and predict subjective color choices, aiming to make creative systems more human-centered. Our dual-track methodology attempts to decouple user preference into “inherent aesthetic profiles” and “contextual design decisions.” Through a dual-level study with 111 participants, we quantified inherent aesthetics into a vector library and trained a Gradient Boosting Decision Tree (GBDT) model on contextual data to predict design choices. The model achieved a predictive accuracy of 40.8%, and a grouped permutation importance analysis revealed the Product Category (Importance = 0.416) as the dominant predictor, providing evidence that design context is paramount. Crucially, a subsequent exploratory user validation study, analyzed with a linear mixed-effects model, showed our personalized recommendations were rated as significantly more satisfying (β = 1.278, p < 0.001) than those of a non-personalized baseline. This research provides a foundational framework for modeling subjective preference by distinguishing between stable traits and dynamic choices, offering a potential pathway to steer creative AI beyond generic outputs towards more personal and context-aware creative partners. Full article

3D-printed hand exoskeletons are important because they enable the creation of affordable, lightweight, and highly customizable assistive and rehabilitation devices tailored to individual patient needs. Their rapid production and design flexibility accelerate innovation, improve access to therapies, and accelerate functional recovery for people with hand impairments. This article discusses the development of a hand exoskeleton using advanced additive manufacturing. It highlights how Industry 4.0 principles such as digital design, automation, and smart manufacturing enable precise prototyping and efficient use of materials. Moving on to Industry 5.0, the study highlights the role of human–machine collaboration, where customization and ergonomics are prioritized to ensure user comfort and rehabilitation effectiveness. The integration of AI-based generative design and digital twins (DTs) is explored as a path to Industry 6.0, where adaptive and self-optimizing systems support continuous improvement. The perspective of personal experience provides insight into practical challenges, including material selection, printing accuracy, and wearability. The results show how technological optimization can be used to reduce costs, improves efficiency and sustainability, and accelerates the personalization of medical devices. The article shows how evolving industrial paradigms are driving the design, manufacture, and refinement of 3D-printed hand exoskeletons, combining technological innovation with human-centered outcomes. Full article

This paper presents a human-centered, AI-driven framework for font design that reimagines typography generation as a collaborative process between humans and large language models (LLMs). Unlike conventional pixel- or vector-based approaches, our method introduces a Continuous Style Projector that maps visual features from a pre-trained ResNet encoder into the LLM’s latent space, enabling zero-shot style interpolation and fine-grained control of stroke and serif attributes. To model handwriting trajectories more effectively, we employ a Mixture Density Network (MDN) head, allowing the system to capture multi-modal stroke distributions beyond deterministic regression. Experimental results show that users can interactively explore, mix, and generate new typefaces in real time, making the system accessible for both experts and non-experts. The approach reduces reliance on commercial font licenses and supports a wide range of applications in education, design, and digital communication. Overall, this work demonstrates how LLM-based generative models can enhance creativity, personalization, and cultural expression in typography, contributing to the broader field of AI-assisted design. Full article

The demand for computing power has increased at a rate never seen before due to the quick development of artificial intelligence (AI) technologies and applications. Consequently, AI data centers, referring to computing facilities specifically designed for large-scale artificial intelligence workloads, have become one of the fastest-growing electricity consumers globally. Therefore, it is essential to understand the load characteristics of AI data centers and their impact on the grid. This paper provides a comprehensive review of the evolving energy landscape of AI data centers. Specifically, this paper (i) presents the energy consumption structure in AI data centers and analyzes the key workload features and patterns in four stages, emphasizing how high power density, temporal variability, and cooling requirements shape total energy use, (ii) examines the impacts of AI data centers for power systems, including impacts on grid stability, reliability and power quality, electricity markets and pricing, economic dispatch and reserve scheduling, and infrastructure planning and coordination, (iii) presents key technological, operational and sustainability challenges for AI data centers, including renewable energy integration, waste heat utilization, carbon-neutral operation, and water–energy nexus constraints, (iv) evaluates emerging solutions and opportunities, spanning grid-side measures, data-center-side strategies, and user-side demand-flexibility mechanisms, (v) identifies future research priorities and policy directions to enable the sustainable co-evolution of AI infrastructure and electric power systems. The review aims to support utilities, system operators, and researchers in maintaining reliable, resilient, and sustainable grid operation in the context of the rapid development of AI data centers. Full article

As artificial intelligence (AI) is increasingly used in classrooms, tutoring systems, and learning platforms, it is essential that these tools are not only powerful, but also easy to understand, fair, and supportive of real learning. Many current AI systems can generate fluent responses or accurate predictions, yet they often fail to clearly explain their decisions, reflect students’ cultural contexts, or give learners and educators meaningful control. This gap can reduce trust and limit the educational value of AI-supported learning. This paper introduces the PEARL framework, a human-centered approach for designing and evaluating explainable AI in education. PEARL is built around five core principles: Pedagogical Personalization (adapting support to learners’ levels and curriculum goals), Explainability and Engagement (providing clear, motivating explanations in everyday language), Attribution and Accountability (making AI decisions traceable and justifiable), Representation and Reflection (supporting fairness, diversity, and learner self-reflection), and Localized Learner Agency (giving learners control over how AI explains and supports them). Unlike many existing explainability approaches that focus mainly on technical performance, PEARL emphasizes how students, teachers, and administrators experience and make sense of AI decisions. The framework is demonstrated through simulated examples using an AI-based tutoring system, showing how PEARL can improve feedback clarity, support different stakeholder needs, reduce bias, and promote culturally relevant learning. The paper also introduces the PEARL Composite Score, a practical evaluation tool that helps assess how well educational AI systems align with ethical, pedagogical, and human-centered principles. This study includes a small exploratory mixed-methods user study (N = 17) evaluating example AI tutor interactions; no live classroom deployment was conducted. Together, these contributions offer a practical roadmap for building educational AI systems that are not only effective, but also trustworthy, inclusive, and genuinely supportive of human learning. Full article

Intangible cultural heritage faces several challenges, including a fragile transmission system, disconnection from modern life, and poor market adaptability. This study takes the Jingsha tenon-and-mortise woodcarving, an important example of Chinese intangible cultural heritage, as a case study to address the issue of the disconnection between traditional craftsmanship and contemporary demands. Methods: A sustainable development model based on user–AIGC–craftsman collaboration is proposed. The research integrates Kano Model and Analytic Hierarchy Process (AHP) based demand analysis, AIGC-generated design solutions, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) evaluation, and Cursor and MCP 3D modeling technologies. The results indicate that this approach reduces design confirmation time from three days to one, minimizes material waste through precise size specifications, and achieves high user satisfaction. The study demonstrates that combining user-centered design with AI-assisted craftsmanship creates a balanced pathway for the sustainability of intangible cultural heritage, while addressing issues of cultural preservation, economic feasibility, and resource efficiency. This tripartite model offers a replicable framework for the sustainable development of traditional crafts globally. Full article

Current ML lifecycle frameworks provide limited support for continuous stakeholder alignment and infrastructure evolution, particularly in sensor-based AI systems. We present CAPTURE, a seven-phase framework (Consult, Articulate, Protocol, Terraform, Utilize, Reify, Evolve) that integrates stakeholder-centered requirements engineering with MLOps practices to address these gaps. The framework was synthesized from four established standards (ISO/IEC 22989, ISO 9241-210, CRISP-ML(Q), SE4ML) and validated through a longitudinal five-year case study of a psychomotor skill learning system alongside semi-structured interviews with ten domain experts. The evaluation demonstrates that CAPTURE supports governance of iterative development and strategic evolution through explicit decision gates. Expert assessments confirm the necessity of the intermediate stakeholder-alignment layer and substantiate the participatory modeling approach. By connecting technical MLOps with human-centered design, CAPTURE reduces the risk that sensor-based AI systems become ungoverned, non-compliant, or misaligned with user needs over time. Full article

Background: Depression remains a major global mental health concern, significantly intensified during the COVID-19 pandemic. As social media usage surged during this period, it emerged as a valuable source for identifying early signs of depression. Artificial intelligence (AI) offers powerful tools to analyze large volumes of user-generated content, enabling timely and effective detection of depressive symptoms. This review aims to preliminarily explore and compare evidence on the use of AI models for detecting depression in social content across the pre-, during, and post-pandemic phases, assessing their effectiveness and limitations. Methods: A narrative literature review was conducted using PubMed and Scopus, following the SANRA guidelines to ensure methodological quality and reproducibility. The study was pre-registered in the OSF database and employed the PICOS framework for the strategy. Inclusion criteria comprised studies in English from the past 10 years that analyzed depression detection via AI, machine learning (ML), and deep learning (DL) applied to textual data, images, and social metadata. This review addresses the following four research questions: (1) whether AI models improved effectiveness in detecting depression during/after the pandemic vs. pre-pandemic; (2) whether textual, visual, or multimodal data approaches became more effective during the pandemic; (3) whether AI models better addressed technical challenges (data quality/diversity) post-pandemic; and (4) whether strategies for responsible AI implementation improved during/after the pandemic. Results: Out of 349 identified records, nine primary studies were included, as most excluded articles had a predominantly technical focus and did not meet the clinical relevance criteria. AI models demonstrated strong potential in detecting depression, particularly through text-based classification and social content analysis. Several studies reported high predictive performance, with notable improvements in accuracy and sensitivity during and after the pandemic, although evidence remains limited. Conclusions: Our preliminary analysis suggests that AI-based depression detection on social media shows potential for clinical use, highlighting interdisciplinary collaboration, ethical considerations, and patient-centered approaches. These findings require confirmation and validation through larger, well-designed systematic reviews. Full article

The application of Generative Artificial Intelligence (GenAI)—defined as a class of AI systems capable of autonomously generating new content such as images, texts, and design solutions based on learned data patterns—has become increasingly widespread in creative design. By supporting ideation, rapid trial-and-error, and data-driven decision-making, GenAI enables designers to explore design alternatives more efficiently and enhances human–computer interaction experiences. In design practice, GenAI functions not only as a productivity-enhancing tool but also as a collaborative partner that assists users in visual exploration, concept refinement, and iterative development. However, users still face a certain learning curve before effectively adopting these technologies. Within the framework of human-centered artificial intelligence, contemporary design practices place greater emphasis on inclusivity across diverse user groups and on enabling intuitive “what-you-think-is-what-you-get” interaction experiences. From a sustainable design perspective, GenAI’s capabilities in digital simulation, rapid iteration, and automated feedback contribute to more efficient design workflows, reduced collaboration costs, and broader access to creative participation for users with varying levels of expertise. These characteristics play a crucial role in enhancing the accessibility of design resources and supporting the long-term sustainability of creative processes. Focusing on the context of China’s digital design industry, this study investigates the application of GenAI in design workflows through an empirical case study of Zhitu AI, a generative design tool developed by Beijing Didi Infinity Technology Development Co., Ltd. The study conducts a literature review to outline the role of GenAI in visual design processes and employs observation-based experiments and semi-structured interviews with users of varying levels of design expertise. The findings reveal key pain points across stages such as prompt formulation, secondary editing, and asset generation. Drawing on the Kano model, the study further identifies potential design opportunities and discusses their value in improving efficiency, supporting non-expert users, and promoting more sustainable and inclusive design practices. Full article

Digital cultural heritage platforms face a dual challenge: preserving rich historical information while engaging an audience with declining attention spans. This paper addresses that challenge by proposing a modular information architecture designed to mitigate cognitive overload in cultural heritage tourism applications. We begin by examining evidence of diminishing sustained attention in digital user experience and its specific ramifications for cultural heritage sites, where dense content can overwhelm users. Grounded in cognitive load theory and principles of user-centered design, we outline a theoretical framework linking mental models, findability, and modular information architecture. We then present a user-centric modeling methodology that elicits visitor mental models and tasks (via card sorting, contextual inquiry, etc.), informing the specification of content components and semantic metadata (leveraging standards like Dublin Core and CIDOC-CRM). A visual framework is introduced that maps user tasks to content components, clusters these into UI components with progressive disclosure, and adapts them into screen instances suited to context, illustrated through a step-by-step walkthrough. Using this framework, we comparatively evaluate personalization and information structuring strategies in three platforms—TripAdvisor, Google Arts and Culture, and Airbnb Experiences—against criteria of cognitive load mitigation and user engagement. We also discuss how this modular architecture provides a structural foundation for human-centered, explainable AI–driven personalization and recommender services in cultural heritage contexts. The analysis reveals gaps in current designs (e.g., overwhelming content or passive user roles) and highlights best practices (such as tailored recommendations and progressive reveal of details). We conclude with implications for designing cultural heritage experiences that are cognitively accessible yet richly informative, summarizing contributions and suggesting future research in cultural UX, component-based design, and adaptive content delivery. Full article

Artificial Intelligence (AI) is increasingly reshaping how cultural heritage institutions design and deliver digital visitor experiences, particularly through adaptive Augmented Reality (AR) applications. However, most existing AR deployments in museums and galleries remain static, rule-based, and insufficiently responsive to visitors’ contextual, behavioral, and emotional diversity. This paper presents ARIA (Augmented Reality for Interpreting Artefacts), a conceptual and architectural framework for AI-supported, adaptive AR experiences in cultural heritage settings. ARIA is designed to address current limitations in personalization, affect-awareness, and ethical governance by integrating multimodal context sensing, lightweight affect recognition, and AI-driven content personalization within a unified system architecture. The framework combines Retrieval-Augmented Generation (RAG) for controlled, knowledge-grounded narrative adaptation, continuous user modeling, and interoperable Digital Asset Management (DAM), while embedding Human-Centered Design (HCD) and Fairness, Accountability, Transparency, and Ethics (FATE) principles at its core. Emphasis is placed on accountable personalization, privacy-preserving data handling, and curatorial oversight of narrative variation. ARIA is positioned as a design-oriented contribution rather than a fully implemented system. Its architecture, data flows, and adaptive logic are articulated through representative museum use-case scenarios and a structured formative validation process including expert walkthrough evaluation and feasibility analysis, providing a foundation for future prototyping and empirical evaluation. The framework aims to support the development of scalable, ethically grounded, and emotionally responsive AR experiences for next-generation digital museology. Full article

As municipalities increasingly adopt artificial intelligence (AI) and generative AI (GenAI) to automate self-service technologies (SSTs), concerns related to fairness, transparency, accountability, and citizen trust have become central to sustainable public-sector governance. While existing studies emphasize either AI adoption or high-level ethical principles, limited empirical research explains how governance mechanisms translate into user-level outcomes in municipal services, particularly in the context of emerging GenAI capabilities. This study addresses this gap by examining how governance antecedents and system design attributes shape user satisfaction, trust, and perceived fairness in AI-enabled municipal SSTs in the United Arab Emirates (UAE). A mixed-methods research design was employed, combining a comparative analysis of international and UAE AI governance frameworks with semi-structured interviews (n = 16) and a survey of municipal employees and service users (n = 272). Qualitative findings reveal persistent concerns regarding data privacy, fairness, explainability, and the absence of standardized municipal-level accountability instruments. Quantitative analysis shows that perceived helpfulness significantly increases user satisfaction, while perceived fairness strongly predicts continued usage intentions. In contrast, system responsiveness exhibits a negative association with satisfaction, highlighting an expectation–performance gap in automated service delivery. Based on these findings, the study proposes a governance–implementation–outcomes model that operationalizes ethical AI principles into measurable governance and service-design mechanisms. Unlike prior adoption-focused or purely normative frameworks, this model empirically links governance instrumentation to citizen-centered outcomes, offering practical guidance for inclusive and sustainable AI and GenAI deployment in municipal self-service systems. The findings contribute to debates on sustainable digital governance by demonstrating how ethically governed AI systems can reinforce public trust, service equity, and long-term institutional resilience. Full article