Search Results (27)

This study scrutinizes the effects of generative artificial intelligence (GenAI) on sustainable performance (SP) in Chinese manufacturing firms through the mediating role of novelty-centered and efficiency-centered business model innovations (BMIs). It also explores the moderating effect of AI regulation on the GenAI–BMIs and GenAI–SP relationships. Data were collected from 1192 middle-level managers across 500 Chinese manufacturing firms using a two-wave survey design. Partial least squares structural equation modeling (PLS-SEM) was employed to test direct, mediating, and moderating relationships. The findings show that GenAI adoption has a significant positive effect on novelty-centered BMI, efficiency-centered BMI and sustainability performance. The GenAI–SP relationship is mediated by both BMIs, indicating that GenAI contributes to sustainability both directly and through innovative business practices. Moreover, AI regulation significantly strengthens the effects of GenAI on both BMI and SP, emphasizing the importance of regulatory alignment in maximizing technological benefits. This research shows that firms should emphasis AI tools and strategies to innovate their business model for better sustainable outcomes. Firms need to follow regulations and rules embedded into digitalization to ensure a sustainable competitive position in the market. Full article

Drawing on the Technological Pedagogical Content Knowledge (TPACK) framework, this qualitative case study investigates how university English teachers integrate AI chatbots into flipped classrooms. Findings reveal that teachers employed chatbots across multiple pedagogical functions—including vocabulary support, grammar explanation, dialogue simulation, and creative content generation—embedded purposefully into both pre-class preparation and in-class collaboration. Rather than passively adopting these tools, teachers strategically positioned chatbots to enhance student autonomy, confidence, and interaction, while tailoring their use to suit specific flipped classroom designs. Meanwhile, teachers acknowledged the risks of over-reliance on AI chatbot content and the disruptions caused by vague or incorrect responses. They responded by developing structured guidance and reforming their roles as facilitators rather than content deliverers. This study contributes new insights into teacher agency in AI-mediated language education, highlighting the complex pedagogical negotiations required to meaningfully integrate emerging technologies into flipped learning environments. Full article

The rapid integration of generative AI, particularly ChatGPT, into academic settings has prompted urgent questions regarding its impact on students’ psychological and academic outcomes. Although generative AI holds considerable potential to transform educational practices, its effects on individual traits such as self-control and academic wellbeing remain insufficiently explored. This study addresses this gap through a sequential two-phase design. In the first phase, the ChatGPT Usage Scale was adapted and validated for a Turkish university student population (N = 413). Using confirmatory factor analysis and item response theory, the scale was confirmed as a psychometrically valid and reliable one-factor instrument. In the second phase, a separate sample (N = 449) was used to examine the relationships between ChatGPT usage, self-control, and academic wellbeing through a mediation model. The findings revealed that higher ChatGPT usage was significantly associated with lower levels of both self-control and academic wellbeing. Additionally, mediation analysis demonstrated that self-control partially mediates the negative relationship between ChatGPT usage and academic wellbeing. The study concludes that while generative AI tools are valuable, their integration into education presents a double-edged sword, highlighting the critical need to foster students’ self-regulatory skills to ensure they can harness these tools responsibly without compromising their academic and psychological health. Full article

This study explores how AI chatbot experiences on travel websites influence consumer engagement, with satisfaction from using AI chatbots as a mediating factor. Grounded in the Stimulus-Organism-Response (S-O-R) framework, the research shifts the focus from utilitarian models to examine how chatbot attributes—e.g., ease of use, information quality, security, anthropomorphism, and omnipresence—affect satisfaction of using AI chatbots and subsequent consumer engagement behaviours. Survey data from 519 Portuguese travellers were analysed using partial least squares structural equation modelling (PLS-SEM). The study contributes to theory by (1) demonstrating S-O-R’s advantages over utilitarian models in capturing relational and emotional dimensions of AI interactions, (2) identifying satisfaction with using AI chatbots as a pivotal mediator between AI chatbot experience and consumer engagement, and (3) revealing generational disparities in drivers of engagement. Notably, satisfaction strongly influences engagement for Generation X, while direct experience matters more for Generation Z. Millennials exhibit a distinct preference for hybrid human–AI service handoffs. The practical implications include prioritizing natural language processing for ease of use, implementing generational customization (e.g., gamification for Gen Z, reliability assurances for Gen X), and ensuring seamless human escalation for Millennials. These insights equip travel businesses to design AI chatbots that foster long-term loyalty and competitive differentiation. Full article

Undergraduate design education faces a structural contradiction characterized by high cognitive load (CL) and relatively low innovation output. Meanwhile, existing generative AI tools predominantly emphasize the generation of visual outcomes, often overlooking the logical guidance mechanisms inherent in design thinking. This study proposes a Dual-Path teaching model integrating critical reconstruction behaviors to examine how AI enhances design thinking. It adopts structured interactions with the DeepSeek large language model, CL theory, and Structural Equation Modeling for analysis. Quantitative results indicate that AI-assisted paths significantly enhance design quality (72.43 vs. 65.60 in traditional paths). This improvement is attributed to a “direct effect + multiple mediators” model: specifically, AI reduced the mediating role of Extraneous Cognitive Load from 0.907 to 0.017, while simultaneously enhancing its investment in Germane Cognitive Load to support deep, innovative thinking. Theoretically, this study is among the first to integrate AI-driven critical reconstruction behaviors (e.g., iteration count, cross-domain terms) into CL theory, validating the “logical chain externalization → load optimization” mechanism in design education contexts. Practically, it provides actionable strategies for the digital transformation of design education, fostering interdisciplinary thinking and advancing a teaching paradigm where low-order cognition is outsourced to reinforce high-order creative thinking. Full article

Amid the nationwide promotion of fitness and the rapid expansion of China’s sports industry, enhancing spectator satisfaction in sports consumption has become a crucial driver for the industry’s sustainable development. Based on the theory of mind-flow perception, this paper explores the influence of stadium intelligent design on race consumption satisfaction, focusing on the four dimensions of stadium intelligent application perception, personality design perception, digital development perception, and technology integration perception, introduces the mind-flow experience as a mediating variable to construct a theoretical model, and analyzes the questionnaire data of 641 spectators with structural equation modeling. The results show that each perception dimension of intelligent design of stadiums has a significant positive effect on consumer satisfaction. Among them, intelligent applications enhance convenience and interactivity, individual design stimulates emotional resonance and immersion, and digital development and technological convergence optimize the audience’s interactive experience through augmented reality, the Internet of Things, and other technologies. flow experience serves as a key mediator to transform functional attributes into emotional value and immersion experience, significantly enhancing satisfaction. This study contributes theoretical insights and managerial guidance for the integration of AI-driven design, human–technology interaction, and smart construction strategies in modern sports venues. The results have broader implications for enhancing digital user environments and optimizing the infrastructure for next-generation event-based urban development. Full article

DIALOGUE (DIagnostic AI Learning through Objective Guided User Experience) is a generative artificial intelligence (GenAI)-based training program designed to enhance diagnostic communication skills in medical students. In this single-arm pre–post study, we evaluated whether DIALOGUE could improve students’ ability to disclose a type 2 diabetes mellitus (T2DM) diagnosis with clarity, structure, and empathy. Thirty clinical-phase students completed two pre-test virtual encounters with an AI-simulated patient (ChatGPT, GPT-4o), scored by blinded raters using an eight-domain rubric. Participants then engaged in ten asynchronous GenAI scenarios with automated natural-language feedback. Seven days later, they completed two post-test consultations with human standardized patients, again evaluated with the same rubric. Mean total performance increased by 36.7 points (95% CI: 31.4–42.1; p < 0.001), and the proportion of high-performing students rose from 0% to 70%. Gains were significant across all domains, most notably in opening the encounter, closure, and diabetes specific explanation. Multiple regression showed that lower baseline empathy (β = −0.41, p = 0.005) and higher digital self-efficacy (β = 0.35, p = 0.016) independently predicted greater improvement; gender had only a marginal effect. Cluster analysis revealed three learner profiles, with the highest-gain group characterized by low empathy and high digital self-efficacy. Inter-rater reliability was excellent (ICC ≈ 0.90). These findings provide empirical evidence that GenAI-mediated training can meaningfully enhance diagnostic communication and may serve as a scalable, individualized adjunct to conventional medical education. Full article

In the era of generative artificial intelligence, writing has evolved into a programmable practice capable of generating sustainable narratives and preserving cultural heritage through poetic prompts. This study proposes “Writing Is Coding ” as a paradigm for sustainability education, exploring how students engage with AI-mediated multimodal creation to address environmental challenges. Using grounded theory methodology with 57 twelfth-grade students from technology-integrated high schools, we analyzed their experiences creating environmental stories and digital cultural artifacts using MidJourney, Kling, and Sora. Data collection involved classroom observations, semi-structured interviews, and reflective journals, analyzed through systematic coding procedures (κ = 0.82). Five central themes emerged: writing as algorithmic design for sustainability (89.5%), emotional scaffolding for environmental awareness (78.9%), aesthetics of imperfection in cultural preservation (71.9%), collaborative dynamics in sustainable creativity (84.2%), and pedagogical value of prompt literacy (91.2%). Findings indicate that AI deepens environmental consciousness and reframes writing as a computational process for addressing global issues. This research contributes a theoretical framework integrating expressive writing with algorithmic thinking in AI-assisted sustainability education, aligned with SDGs 4, 11, and 13. Full article

This study investigated pre-service teachers’ (PTs) intentions to adopt generative AI (GenAI) tools in future classrooms by applying an extended Technology Acceptance Model (TAM). Participants were enrolled in multiple teacher-preparation programs within a single U.S. higher education institution. Through a structured GenAI-integrated activity using Khanmigo, a domain-specific AI platform for K-12 education, PTs explored AI-supported instructional tasks. Post-activity data were analyzed using PLS-SEM. The results showed that perceived usefulness (PU), perceived ease-of-use (PEU), and self-efficacy (SE) significantly predicted behavioral intention (BI) to adopt GenAI, with SE also influencing both PU and PEU. Conversely, personal innovativeness in IT and perceived cyber risk showed insignificant effects on BI or PU. The findings underscored the evolving dynamics of TAM constructs in GenAI contexts and highlighted the need to reconceptualize ease-of-use and risk within AI-mediated environments. Practically, the study emphasized the importance of preparing PTs not only to operate AI tools but also to critically interpret and co-design them. These insights inform both theoretical models and teacher education strategies, supporting the ethical and pedagogically meaningful integration of GenAI in K-12 education. Theoretical and practical implications are discussed. Full article

Ischemic stroke ranks among the top global causes of disability and mortality, with a highly dynamic pathological process. Post-stroke neuroinflammation, mediated by microglia, demonstrates a dual role in both injury and repair. The CX3CR1/CX3CL1 signaling axis, highly expressed in microglia, acts as a key regulator. This review examines the spatiotemporal dynamics of the axis across the stroke process and its involvement in neural repair. Crucially, this signaling pathway demonstrates stage-dependent functional duality: its cellular sources, receptor expression profiles, and functional consequences undergo temporally orchestrated shifts, manifesting coexisting or interconverting protective and damaging properties. Ignoring this dynamism compromises the therapeutic efficacy of targeted interventions. Thus, we propose a triple precision strategy of “stroke phase—biomarker—targeted intervention”. It uses specific biomarkers for precise staging and designs interventions based on each phase’s signaling characteristics. Despite challenges like biomarker validation, mechanistic exploration, and cross-species differences, integrating cutting-edge technologies such as spatial metabolomics and AI-driven dynamic modeling promises to shift stroke therapy toward personalized spatiotemporal programming. Temporally targeting CX3CR1 signaling may offer a key basis for developing next-generation precision neural repair strategies for stroke. Full article

Generative Artificial Intelligence (GenAI)-assisted Virtual Reality (VR) heritage serious game design constitutes a complex adaptive socio-technical system in which natural language prompts act as control levers shaping designers’ cognition and action. However, the systemic effects of prompt type on agency construction, decision boundaries, and process strategy remain unclear. Treating the design setting as adaptive, we captured real-time interactions by collecting think-aloud data from 48 novice designers. Nine prompt categories were extracted and their cognitive effects were systematically analyzed through the Repertory Grid Technique (RGT), principal component analysis (PCA), and Ward clustering. These analyses revealed three perception profiles: tool-based, collaborative, and mentor-like. Strategy coding of 321 prompt-aligned utterances showed cluster-specific differences in path length, first moves, looping, and branching. Tool-based prompts reinforced boundary control through short linear refinements; collaborative prompts sustained moderate iterative enquiry cycles; mentor-like prompts triggered divergent exploration via self-loops and frequent jumps. We therefore propose a stage-adaptive framework that deploys mentor-like prompts for ideation, collaborative prompts for mid-phase iteration, and tool-based prompts for final verification. This approach balances creativity with procedural efficiency and offers a reusable blueprint for integrating prompt-driven agency modelling into GenAI design workflows. Full article

Chimeric antigen receptor (CAR)-engineered natural killer (NK) cells are a promising platform for off-the-shelf immunotherapy due to their safety advantages over CAR-T cells, including lower risk of graft-versus-host disease, cytokine release syndrome, and neurotoxicity. However, their persistence and efficacy are limited by immunological challenges such as host T-cell-mediated rejection, NK cell fratricide, and macrophage-mediated clearance. This review summarizes gene editing strategies to overcome these barriers, including β2-microglobulin (B2M) knockout and HLA-E overexpression to evade T and NK cell attacks, CD47 overexpression to inhibit phagocytosis, and TIGIT deletion to enhance cytotoxicity. In addition, we discuss functional enhancements such as IL-15 pathway activation, KIR modulation, and transcriptional reprogramming (e.g., FOXO1 knockout) to improve persistence and antitumor activity. We also highlight the role of induced pluripotent stem cell (iPSC)-derived NK platforms, enabling standardized, scalable, and multiplex gene-edited products. Finally, we explore artificial intelligence (AI) applications in immunogenomic profiling and predictive editing to tailor NK cell therapies to patient-specific HLA/KIR/SIRPα contexts. By integrating immune evasion, functional reinforcement, and computational design, we propose a unified roadmap for next-generation CAR-NK development, supporting durable and broadly applicable cell-based therapies. Full article

This study investigates how pre-service Earth science teachers used ChatGPT in designing lessons based on the 5E instructional model and what educational opportunities and challenges emerged. As generative AI tools gain traction in education, understanding their integration into science lesson planning is increasingly important. Eight pre-service teachers from a South Korean university participated in a four-week instructional design project. They developed 5E-aligned Earth science lessons while interacting with ChatGPT for idea generation, explanation, activity development, and assessment. Data sources included lesson plans, ChatGPT interaction logs, reflective journals, and interviews. Thematic analysis was used to examine instructional uses of AI and the adaptations required during the process. Findings showed that ChatGPT supported different phases of the 5E model—providing metaphors and analogies in Engage, activity ideas in Explore, draft explanations in Explain, task prompts in Elaborate, and assessment questions in Evaluate. However, participants frequently revised or rejected AI-generated content to match inquiry goals, student readiness, and curriculum standards. The study highlights the importance of pedagogical reasoning in AI-supported lesson design. It contributes to the growing literature on teacher education and AI by offering a phase-specific view of GenAI use and underscoring the instructional mediation needed for effective application. Full article

This study explores how the sequence and timing of Industry 4.0 technology adoption affect sustainable innovation in manufacturing firms. Using longitudinal data from the State Society of Industrial Participations, we track the adoption patterns of eight technologies, including industrial IoT, cloud computing, RFID, machine learning, robotics, additive manufacturing, autonomous robots, and generative AI. Sequence analysis reveals five distinct adoption profiles: data-centric foundations, automation pioneers, holistic integrators, cautious adopters, and product-centric innovators. Our results show that these adoption pathways differentially impact sustainability outcomes such as circular material innovation, energy transition, operational eco-efficiency, and emissions reduction. Mediation analysis indicates that data orchestration capabilities significantly enhance resource productivity in holistic integrators, generative design competencies accelerate biomaterial innovation in product-centric innovators, and cyber-physical integration reduces lifecycle emissions in automation pioneers. By highlighting how temporal complementarities among technologies shape sustainability performance, this research advances dynamic capabilities theory and emphasizes the path-dependent nature of sustainable innovation. The findings provide practical guidance for firms to align digital transformation with sustainability objectives and offer policymakers insights into designing timely support mechanisms for industrial transitions. This work bridges innovation timing with ecological modernization, contributing a new understanding of capability development for sustainable value creation. Full article

The integration of artificial intelligence (AI) chatbots in online learning environments has transformed the way students engage with educational content, offering personalised learning experiences, instant feedback, and scalable support. This study investigates the role of AI-driven chatbots in the Pedagogical Information and Communication Technology (ICTPED) Massive Open Online Course (MOOC), a professional development course aimed at enhancing teachers’ Professional Digital Competence (PDC). The study pursues two connected aims: (1) to examine how chatbots support content comprehension, self-regulated learning, and engagement among pre- and in-service teachers, and (2) to explore, through a cultural-historical perspective, how chatbot use contributes to the development of students’ digital agency. Based on data from 46 students, collected through structured questionnaires and follow-up interviews, the findings show that chatbots functioned as interactive learning partners, helping students clarify complex concepts, generate learning resources, and engage in reflection—thereby supporting their PDC. At the same time, chatbot interactions mediated learners’ development of digital agency, enabling them to critically interact with digital tools and navigate online learning environments effectively. However, challenges such as over-reliance on AI-generated responses, inclusivity issues, and concerns regarding content accuracy were also identified. The results underscore the need for improved chatbot design, pedagogical scaffolding, and ethical considerations in AI-assisted learning. Future research should explore the long-term impact of chatbots on students’ learning and the implications of AI-driven tools for digital agency development in online education. Full article