Search Results (390)

Engineering design education is an effective instructional approach for enhancing students’ motivation, interest, and creativity while introducing them to the engineering design process (EDP). However, there is limited knowledge on how to integrate the EDP into robotics education, particularly AI-robotics, and how students experience AI-enabled robotics project-based learning grounded in an EDP. This pre-/posttest embedded mixed-methods study adds to the scarce body of literature on interdisciplinary education in engineering design, robotics, and AI. This project developed, implemented, and evaluated a project-based engineering design AI-robotics curriculum that introduced novice Computer Science (CS) high school students to robotics, machine learning, and AI. Students’ collaborative robotics projects were grounded in an EDP to introduce the students to engineering practices and promote engagement and interest through design-based, hands-on learning. An analysis of quantitative and qualitative data revealed an improvement in students’ CS attitudes, collaboration, and social interactions after participating in the curriculum. Recommendations for designing AI-robotics projects grounded in an EDP are discussed. Full article

Fire safety in high-rise buildings represents a critical challenge in the United Arab Emirates (UAE), where intensive urbanization, extreme climatic conditions, and multilayered regulatory frameworks impose unique competency demands on architects and Fire Safety Engineering (FSE) consultants. Despite this, no empirically validated competency framework exists that simultaneously addresses both professional groups and is tailored to the specificities of the UAE context. This study aimed to construct and empirically validate such a framework. A three-phase sequential exploratory mixed-method design was employed. In the first phase, a systematic literature review yielded a preliminary set of 69 competency indicators organized within a Knowledge, Skills and Attitudes (KSA) structure. In the second phase, a three-round Delphi technique with an expert panel of 18 specialists validated the set to 62 final indicators. In the third phase, importance–performance analysis (IPA) was conducted on a sample of 250 professionals actively engaged in fire safety projects across four UAE. IPA identified 16 priority competency gaps, most pronounced in digital transformation (BIM, CFD, AI; gap = 1.23), proactive client advisory competencies (gap = 1.21), and regulatory navigation and Civil Defence coordination (gap = 1.00). A counterintuitive finding emerged whereby architects systematically rated competencies higher than FSE consultants across all dimensions (all p < 0.05). Psychometric validation confirmed excellent instrument reliability (Cronbach’s Alpha > 0.95) and a theoretically consistent three-factor KSA structure explaining 70.06% of variance. The developed framework of 62 empirically validated indicators represents the first competency model of its kind for architects and FSE consultants in the Gulf Cooperation Council (GCC) region. Its findings provide a direct empirical basis for curriculum reform, Continuing Professional Development (CPD) programmes, and professional licencing standards in the UAE and across the GCC region. The study makes three original contributions: the first empirically validated UAE-specific competency framework for these professional groups; a methodological combination of Delphi, IPA, EFA, Mann–Whitney, and Kruskal–Wallis not previously applied in fire safety competency research; and empirical confirmation that 74% of indicators required original development or adaptation, demonstrating the limitations of generic international competency models in the UAE context. Full article

This study explores the multifaceted attitudes of high school students toward the use of artificial intelligence (AI) and large language models (LLMs) like ChatGPT in educational contexts. Drawing upon a tripartite model of attitudes, our research evaluates affective, cognitive, and behavioral dimensions to offer a nuanced understanding of students’ perceptions. The affective dimension assesses emotional responses to AI tools, the cognitive dimension examines beliefs about the utility and ethical considerations of AI, and the behavioral dimension evaluates actual usage patterns of AI technologies. Utilizing a newly developed survey instrument tailored for the educational context, data was collected from 93 high school students across different regions of Italy in the period that ranged from February 2024–March 2024. Exploratory factor analysis (EFA) was employed to explore the underlying structure of the survey instrument and identify underlying factors influencing AI acceptance. The analysis reveals three distinct factors—Mindful AI Learning, Embracing AI Effects, and LLM as Learning Companion, highlighting the complexity of students’ attitudes toward AI. Results indicate a cautious but optimistic reception of AI in education, offering crucial insights into Information Intelligence for enhanced learning and the design of personalized learning pathways. The study contributes to the literature by offering a novel scale to measure attitudes toward artificial intelligence, specifically focusing on both general AI and Generative AI large language models, such as ChatGPT. Moreover, it highlights the critical need for AI literacy, ethical digital learning frameworks, and robust institutional policies to bridge the digital divide. Consequently, this work is framed as a preliminary exploratory investigation. Ultimately, these findings advance our knowledge of transformative digital learning processes and inform future strategies for human–machine integration in educational systems. Full article

The existing literature highlights that artificial intelligence (AI) creates both hope and threat perceptions among managers and workers, particularly due to concerns about potential job losses and the negative effect on continued professional development. Employee trust in AI-based systems varies depending on their features and performance. Furthermore, regardless of the performance of such systems, some individuals are inherently opposed to AI, a phenomenon known as AI aversion. In this study, an Integrative AI Adoption Framework is developed, drawing upon principles from established theories, including the technology acceptance model, behavioral decision theory, and emotion-based frameworks, to assess how perceived usefulness and perceived ease of use, along with perceived threat, trust, and AI aversion, influence human resources (HR) professionals’ attitudes and behavioral intentions to use AI-based recruitment systems. In doing so, the study conceptualizes AI-based recruitment as a socio-technical system in which a technical subsystem (the system’s instrumental and AI-specific properties) and a social subsystem (the affective and trust-related responses of HR professionals) must be jointly considered to explain adoption. The model was tested using the partial least squares structural equation modeling (PLS-SEM) approach through survey-based data collected from 242 HR professionals. The study’s findings indicate that attitude plays an important role in shaping behavioral intention, and perceived usefulness is a key driver of attitude. AI aversion negatively influences attitudes, while trust has a twofold effect of reducing AI aversion and positively influencing attitude. Additionally, perceived threat significantly increases AI aversion, which is driven by concerns over job replacement and personal development. Full article

AI use is associated with both cognitive relief and cognitive offloading, leading to uncertainty in how users make value judgments and decisions. This study focuses on in-class AI use and explores the perceptions of cognitive relief and cognitive offloading among university students, as well as the distinct mediating mechanisms through which these factors shape the attitudes of students. Based on questionnaire data from 287 respondents, structural equation modeling and bootstrap methods were employed to test the research hypotheses. The results show that cognitive relief exerts a complementary mediating effect between AI use and attitudes, whereas cognitive offloading functions as a competitive mediator. The two mechanisms produce opposing effects on students, with cognitive relief demonstrating a stronger overall mediating effect. These findings suggest that educators should guide students toward a more nuanced understanding of AI use to mitigate confusion and its potential negative psychological consequences. Moreover, educators and institutions should leverage AI to provide cognitive relief for higher-order learning activities, thereby enhancing the engagement, motivation, and deeper learning processes of students, while simultaneously implementing reflective and critical thinking practices to guard against the risks of cognitive offloading. This study is limited by its single-institution convenience sample and reliance on self-reported data; future research incorporating qualitative methods such as interviews and classroom observations is encouraged to further validate and extend these findings. Full article

Despite rapid growth in the AI endorser market, the psychological mechanisms governing their effectiveness remain theoretically fragmented. This study proposes the AHICC (AI–Human Interface in Consumer Cognition) model—integrating the Stereotype Content Model, Uncanny Valley hypothesis, anthropomorphism theory, Source Credibility Model, and Parasocial Interaction theory—to explain consumer responses to AI endorsers. A fully crossed 3 (endorser type: AI vs. hybrid vs. human) × 3 (anthropomorphism level: low vs. moderate vs. high) × 2 (technological transparency: low vs. high) between-subjects factorial experiment (n = 252) was conducted. Twenty-one sub-hypotheses were tested using MANOVA, polynomial regression, SEM, and bootstrap mediation analysis. All 21 sub-hypotheses were supported. AI endorsers outperformed human counterparts on brand attitude and purchase intention. Polynomial regression confirmed an inverted U-shaped Uncanny Valley effect with an optimal anthropomorphism level of 4.7 (7-point scale). High technological transparency attenuated the Uncanny Valley effect by approximately 60%. Dual-pathway mediation through cognitive and affective routes was confirmed, and TRI and product complexity emerged as significant boundary conditions. The AHICC model offers the first comprehensive framework for the AI endorser context, providing theoretically grounded guidance on anthropomorphism calibration, transparency strategy, and product-category-specific endorser selection. Full article

Artificial Intelligence (AI) is rapidly transitioning from a specialized technology to an everyday socio-technical infrastructure, yet public acceptance remains shaped by a trade-off between perceived benefits and risks. This study examines how individuals from varied demographic and professional backgrounds perceive, use, and evaluate AI-enabled systems using a mixed-method research design. A bilingual (English/Arabic) online survey ($N=115$) captured demographics, awareness, usage patterns, perceived impact, self-assessed understanding, domain-specific trust, concerns, and attitudes toward regulation, complemented by open-ended reflections. In parallel, semi-structured face-to-face interviews provided deeper insight into AI conceptualization, lived experiences, trust boundaries, and conditions for acceptable use. Quantitative results show frequent AI engagement embedded in daily life, with strong domain dependence in trust: education is the most trusted domain, whereas healthcare and finance attract substantially lower trust. Prominent concerns include overreliance (“brain rot”), privacy and data misuse, job displacement, and misinformation. Support for stronger AI regulation is high, indicating that governance is viewed as a prerequisite for sustainable adoption rather than a constraint on innovation. Qualitative findings triangulate these results, revealing a pattern of conditional acceptanceunderstood as the simultaneous valuation of AI’s practical utility alongside the imposition of explicit trust prerequisites whereby participants value AI for productivity and learning support while emphasizing confidentiality, transparency, human oversight in high-stakes contexts, and clear boundaries to mitigate misuse and erosion of human judgment. The study offers empirically grounded insights for policymakers, educators, and industry stakeholders into how AI acceptance is negotiated through utility, literacy, perceived risk, and expectations of accountability. Full article

Background: Large language models and other generative artificial intelligence (AI) tools are increasingly being embedded in digital healthcare services, including mobile health applications, telerehabilitation, remote monitoring, and hybrid care pathways. In this review, digital healthcare refers to technology-mediated healthcare services in which digital platforms, mobile applications, wearables, remote communication, and AI-enabled interfaces support health assessment, self-management, rehabilitation, clinical decision support, or service delivery. When AI-generated exercise guidance moves from general education to individualized recommendations about dose, progression, contraindications, or rehabilitation, it may become directly actionable and safety-relevant. Objectives: This review aimed to clarify when AI-generated exercise guidance in digital healthcare may warrant safety-relevant governance attention and to outline implementation considerations for explainability, human oversight, and service-level governance. It addresses a gap in the literature: general AI-governance and exercise-prescription discussions rarely specify how point-of-use explanations, review thresholds, and escalation safeguards can be organized for directly actionable AI exercise guidance. Methods: We conducted a governance-oriented narrative review of peer-reviewed literature and representative regulatory or guidance documents. This review was not designed as a systematic review, scoping review, or exhaustive evidence map; transparent source mapping was used to support conceptual synthesis. Searches and source mapping focused on generative AI, large language models, explainable AI, clinical decision support, digital health, mobile health, exercise prescription, rehabilitation, trust, automation bias, and human oversight. Sources were included when they informed the safety, explainability, governance, or real-world implementation of patient-facing AI-generated exercise guidance. Extracted material was grouped by evidentiary role and synthesized through framework synthesis and governance mapping to distinguish literature-supported observations, author interpretation, and proposed implementation tools. Results: The included sources were first organized into five thematic groups: digital exercise delivery and exercise-prescription evidence; explainability, trust, and automation bias literature; professional responsibility, ethics, and patient disclosure literature; regulatory and policy documents; and digital literacy, patient/clinician attitudes, and equity literature. The synthesis then proceeded from safety relevance to explanation needs, human oversight and escalation needs, and selected regulatory and policy signals before translating these strands into conceptual and implementation-oriented outputs rather than empirically validated instruments. AI-generated exercise guidance was most safety-relevant in scenarios involving individualized dose, progression, contraindication-sensitive action, or rehabilitation strategy. Across the included sources, generic transparency alone was not sufficient to support reviewable use; relevant explanation elements included evidence sources, risk warnings, reasoning paths, and reasonable alternatives. Oversight considerations varied with embodied risk, clinical ambiguity, user vulnerability, and likelihood of direct enactment. Implementation considerations linked interface design, clinical review, escalation, auditability, and post-deployment monitoring. Conclusions: AI-generated exercise guidance in digital healthcare may warrant governance attention as a patient-safety and accountability issue when it influences actionable exercise decisions. The proposed framework offers a conceptual basis for designing more reviewable and accountable mobile and remote exercise-support services. Future work can validate these outputs in patient-facing services, clinician review workflows, usability studies, implementation pilots, and safety evaluations. Full article

Adolescence is an important period for developing oral health literacy (OHL) and oral health-related knowledge, attitudes, and practices (KAP). Artificial intelligence-based virtual human oral education (AI-VOHE) has been proposed as a tool for school-based oral health education (OHE); however, evidence regarding its educational outcomes among adolescents remains limited. This pilot study compared short-term changes in OHL and self-reported oral health-related KAP between AI-VOHE and conventional face-to-face oral health education (FOHE). A pilot cluster-randomized pre-test–post-test intervention design was employed in two middle schools. Participants received either AI-VOHE or FOHE, and outcomes were assessed immediately before and after two educational sessions using a structured self-administered questionnaire. A total of 268 adolescents were included in the analyses. Linear mixed-effects models were used to evaluate the effects of time, group, and the group-by-time interaction. Both groups showed significant short-term improvements in OHL and self-reported oral health-related KAP following the intervention (all p < 0.05). However, no significant group-by-time interaction effects were observed for any outcome (all p > 0.05), indicating comparable short-term effectiveness between AI-VOHE and FOHE. These findings suggest that AI-VOHE showed short-term improvements in adolescents’ OHL and self-reported oral health-related KAP comparable to those achieved with FOHE. Given the pilot nature of the study, the limited number of schools, and the absence of long-term follow-up, the findings should be interpreted cautiously. Further adequately powered cluster-randomized trials are warranted. Full article

Prior research focused mainly on exploring the implementations of Artificial Intelligence (AI) in efficient and eco-friendly hotel operations, with very limited studies investigating the vast capabilities of AI, such as AI-generated visual information, in shaping green attitudes and behaviors among hotel guests. Therefore, this study addresses such a gap testing the potential contributions of AI-generated visual images to hotel guests’ eco-friendly attitudes and behaviors. Explicitly, the study adopts a stimulus-based approach to examine the effects of the perceived persuasiveness of AI-generated stimulating images on guests’ green attitudes, willingness to sacrifice for the environment, and green citizenship. Moreover, it investigates the perceived authenticity of AI-generated visual information as a moderator of these linkages. A stimulus-based questionnaire was developed and administered among hotel guests. The survey was Web-based and included four AI-generated images encouraging eco-friendly attitudes and behaviors, followed by scales measuring the study constructs. The sample of the study included 428 participants who were enlisted using a convenience sampling technique. Data analysis included the performance of a PLS-SEM using Smart PLS 3.0 to test the postulated hypotheses. The findings of the study reveal that AI-generated visuals positively contributed to guests’ attitudes, willingness to sacrifice for the environment, and green citizenship behaviors. The perceived authenticity of the generated visuals also exerted a moderating effect on the examined linkages. Overall, this research fills a gap in knowledge and provides practical implications that support more sustainable attitudes and behaviors among hotel guests. Full article

As AI permeates daily life, algorithmic platforms increasingly function as complex sociotechnical systems that shape public perception and societal attitudes. Addressing concerns that AI text-to-image models and search engines reinforce stereotypes, this study focuses on China, a context marked by traditional gender norms and a vast technological ecosystem, examining how algorithmic systems perpetuate gender power structures through occupational representations. Using algorithmic audits of 60 occupations, Z-tests, and QAP network analysis, this study compares platform gender representations with national census data, systematically distinguishing “generative bias” in AI platforms (Doubao Seedream 3.0, Jimeng Image 3.0) from “retrieval bias” in search engines (Baidu, Sogou). Findings reveal that search engines reinforce stereotypes by over-representing dominant genders and obscuring non-mainstream ones. Generative AI exhibits more radical distortions. The specialized AI Jimeng shows a strong gender polarization feature, while the general AI Doubao shows an ideal balanced gender presentation tendency, balancing representation yet creating an equally false reality. Compared to search engines, AI platforms have greater creativity in representing occupational gender. This study reveals a mutually reinforcing bias cycle among audiences, media, and algorithms, offering a crucial non-Western perspective for feminist technology studies and significant implications for equitable AI governance. Full article

The shift to teleworking, defined as technology-enabled work arrangements in which employees perform organizational tasks remotely outside traditional office settings, has exposed the limitations of traditional performance management systems, including the lack of direct oversight, micromanagement risks, communication barriers, and employee isolation and well-being. These systems often rely on physical presence or intrusive surveillance rather than outcome-based evaluation. This paper asks how AI-driven performance management can be designed to address the documented challenges of teleworking while safeguarding employee autonomy, fairness, and well-being. The study integrates a comprehensive literature review on AI capabilities with empirical evidence from a sequential mixed-methods study of Canadian public servants, comprising machine learning analysis of over 205,000 tweets, document analysis of federal and provincial teleworking policies, a survey of 176 public servants analyzed using logistic regression, and semi-structured interviews with Government of Canada employees. Grounded in socio-technical theory and the Theory of Planned Behavior, the findings reveal that organizational support, workplace socialization, and attitudes are stronger predictors of teleworking success than digital skills or monitoring, while isolation functions as a measurable risk factor. These empirical patterns are mapped to specific AI capabilities to produce a socio-technical framework organized around three interdependent layers: technological, organizational, and human-centered. The paper contributes an empirically grounded alternative to purely speculative treatments of AI in performance management, offering design requirements derived from what teleworkers actually experience rather than from technological possibilities alone. While the framework is analytically grounded in empirical evidence, behavioral theory, and existing AI capabilities, it has not yet undergone full technical or longitudinal organizational validation. Accordingly, it should be understood as a theoretically and empirically informed design artifact intended to guide future implementation and evaluation efforts. It is worth acknowledging that the study’s key limitations include a Canada-specific public sector sample, modest survey and interview sizes, and the exploratory nature of several proposed AI capabilities; future cross-sectoral, comparative, and longitudinal research is needed to validate and extend the framework. Full article

Climate change is significantly impacting sustainable agriculture and poses a threat that is likely to motivate farmers to adapt by applying AI technology to reduce risks, costs, expenses, and the impact on greenhouse gas emissions. In other contexts related to climate change, it is important to assess whether perceived climate threats and perceived vulnerability to climate change influence farmers’ intention to use artificial intelligence and whether farmers believe AI is an effective method for addressing climate change, as well as their confidence in its effectiveness. This research examines whether the ability to learn about AI independently affects the intention to use AI, aligning with Protection Motivation Theory. It further evaluates whether perceived ease of use of AI influences perceived usefulness, considering the core factors of perceived ease of use and perceived usefulness based on the Technology Acceptance Model as influencing the intention to use AI. Furthermore, it investigates whether PEOU (Perceived ease of use) and PU (Perceived usefulness) affect attitude (a key factor in the Theory of Planned Behavior) and subjective norm (another core factor in TPB (Theory of Planned Behavior)) influencing farmers’ behavioral adaptation to AI use. Therefore, exploring farmers’ behavioral intention to use AI integrates three theories: PMT (Protection Mo-tivation Theory), TPB, and TAM (Technology Acceptance Model), presenting them as a conceptual model to examine the motivating factors influencing behavioral change. This research surveyed 471 farmers in Thailand using data analyzed from PLS-SEM (Partial Least Squares Structural Equation Mod-eling). The findings revealed that only eight hypotheses (AI self-efficacy, PEOU, PU, ATT (Attitude), and SN (Social Norm)) significantly influenced the intention to use AI, while three hypotheses (PS (Perceived severity), PV (Perceived vulnerability), and RE (Response efficacy)) did not. This will be useful for planning or strategizing AI adoption among farmers, focusing on reducing problems and obstacles from insignificant factors to achieve sustainable agriculture and minimize the impact that may lead to inequality from AI use, or the AI divide, in the future. Full article

Artificial intelligence is rapidly entering higher education, yet many students experience anxiety when learning to use it. This study examines how performance expectations, perceived explainability, and perceived ethical risks shape two algorithm attitudes, algorithm aversion and algorithm appreciation, and how these attitudes influence artificial intelligence learning anxiety. Using a hybrid partial least squares structural equation modeling–artificial neural network (PLS-SEM–ANN) approach, this study analyzed survey data from 409 university students. Results show that both algorithm aversion and algorithm appreciation significantly increase artificial intelligence learning anxiety, although the effect of algorithm aversion is much stronger, supporting an approach–avoidance account. Perceived ethical risk is the strongest predictor of algorithm aversion but has no significant effect on algorithm appreciation. By contrast, performance expectations and perceived explainability strengthen algorithm appreciation while also showing weaker positive effects on algorithm aversion. These findings suggest that, in educational settings, stronger performance value and greater explainability do not simply reassure students; they can also increase pressure by making errors, responsibility, and the need to use artificial intelligence effectively more salient. The artificial neural network results corroborate these patterns. This study extends research on algorithm attitudes and offers guidance for creating more supportive artificial intelligence learning environments. Full article

Artificial intelligence (AI) has transformed programmatic advertising (PA) by enabling automated, data-driven personalization within digital marketing ecosystems. However, this transformation has also intensified concerns regarding privacy, consumer trust, and the long-term sustainability of these ecosystems. In this study, sustainability is conceptualized as the capacity of digital marketing systems to maintain long-term functionality without eroding consumer trust or amplifying privacy-related tensions that may undermine system stability over time. This study addresses the research question: How does AI-driven PA affect the sustainability of digital marketing ecosystems through the personalization–privacy paradox? A systematic literature review, conducted in line with PRISMA guidelines, synthesizes prior research across four interrelated clusters: AI-driven personalization, privacy concerns, consumer trust, and consumer attitudes and behavior. The findings indicate that consumer responses to AI-driven personalization are not linear and are shaped by factors such as transparency, perceived control, and the perceived legitimacy of data practices. The review further shows that the paradox of personalization and privacy operates as a persistent condition within AI-driven advertising ecosystems, where the benefits of personalization coexist with ongoing privacy-related tensions. This study contributes to the literature by synthesizing fragmented research on AI-driven PA and by highlighting the central role of trust and transparency in understanding how personalization and privacy tensions shape consumer responses in digital marketing ecosystems. Full article