Search Results (17,608)

This study examines the ethical problems of using artificial intelligence (AI) applications in higher education, focusing on activities performed by university educators. Drawing on Slovak legislation that defines educators’ responsibilities, the study classifies their activities into three categories: teaching, scientific research, and other (academic management and self-directed professional development). From standpoint of methodology, a thematic review of 42 open-access, peer-reviewed articles published between 2022 and 2025 was conducted across the Web of Science and Scopus databases. Relevant AI applications and their associated ethical issues were identified and thematically categorized. Results of this study show that AI applications are extensively used across all analysed areas of university educators’ activities. Most notably used are applications that are generative language models, editing and paraphrasing tools, learning and assessment software, management and search tools, visualizing and design tools, and analysis and management systems. Their adoption raises ethical concerns which can be thematically grouped into six categories: privacy and data protection, bias and fairness, transparency and accountability, autonomy and oversight, governance gaps, and integrity and plagiarism. The results provide universities with a structured analytical framework to assess and address ethical risks related to AI use in specific academic activities. Although the study is limited to open-access literature, it offers a conceptual foundation for future empirical research and the development of ethical, institutionally grounded AI policies in higher education. Full article

Against the backdrop of the global integration of green transformation and the digital economy, how manufacturing enterprises leverage digitalisation to drive green innovation has become a focal point for both academic and industrial sectors. Based on the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), this study constructs a moderated mediation model to explore the internal mechanism through which digital transformation influences green innovation via green dynamic capabilities and examines the boundary role of environmental munificence. Questionnaire data, collected in two stages from 312 Chinese manufacturing enterprises using SPSS 27.0 and AMOS 24.0, was analysed, and the empirical results indicate that digital transformation not only directly promotes green innovation but also exerts an indirect influence through the three dimensions of green dynamic capabilities: insights into the capability of green opportunities, green resource integration, and green resource reconstruction. Furthermore, environmental munificence significantly and positively moderates the relationship between green dynamic capabilities and green innovation, suggesting that this relationship is strengthened in resource- and opportunity-rich environments. Path analysis of the three green dynamic capability dimensions reveals that back-end capabilities (resource integration and reconfiguration) have a more pronounced impact on green innovation than front-end capabilities (opportunity insights). From the dual perspectives of capability building and contextual fit, this study elucidates the mechanism and boundary conditions of digital transformation driving green innovation, enriches green innovation theory, and offers practical insights into the digital-green transformation of manufacturing enterprises. Full article

Industry 4.0 (I4.0) promises that technological advances are happening at an accelerating rate, which is pushing all industries to undergo digital transformation to boost competitiveness, productivity, and business efficiency. As industrial companies transition to Industry 4.0, one of the maturity models that helps them identify opportunities is the Smart Industry Readiness Index (SIRI). SIRI is in line with other international manufacturing initiatives and has the potential to become a global standard for the manufacturing sector’s future. To achieve market competitiveness, smart manufacturing requires the end-to-end integration of Industry 4.0 technologies and SIRI. The successful implementation of such a comprehensive integration depends on carefully selecting the I4.0 technologies to conform to industry requirements. The Influences of I4.0 technologies on SIRI are not clearly outlined in any of the earlier research. Thus, employing a dependable Multi-Criteria Decision Making (MCDM) methodology using fuzzy TOPSIS, this article aims to analyze the influence of Industry 4.0 technologies on SIRI from the perspectives of both academic and industry experts. Expert opinions were gathered on the relationship between SIRI and I4.0 technologies. TOPSIS utilizes fuzzy theory to address the ambiguity and uncertainty inherent in human judgment. The findings showed that the best I4.0 technology for SIRI is the cyber-physical system (CPS). Full article

Conventional academic warning systems in higher education often rely on end-of-semester grades, which severely limits opportunities for timely intervention. To address this, our interdisciplinary study developed and validated a comprehensive socio-technical framework that integrates social-cognitive theory with learning analytics. The framework combines educational data mining with culturally responsive, personalized interventions tailored to a non-Western context. A two-phase mixed-methods design was employed: first, predictive models were built using Learning Management System (LMS) data from 2,856 students across 64 courses. Second, a quasi-experimental trial (n = 48) was conducted to evaluate intervention efficacy. Historical academic performance, attendance, and assignment submission patterns were the strongest predictors, achieving a Balanced Area Under the Curve (AUC) of 0.85. The intervention, specifically adapted to Confucian educational values, yielded remarkable results: 73% of at-risk students achieved passing grades, with a large effect size for academic improvement (Cohen’s d = 0.91). These findings empirically validate a complete prediction–intervention–evaluation cycle, demonstrating how algorithmic predictions can be effectively integrated with culturally informed human support networks. This study advances socio-technical systems theory in education by bridging computer science, psychology, and educational research. It offers an actionable model for designing ethical and effective early warning systems that balance technological innovation with human-centered pedagogical values. Full article

The adoption of artificial intelligence has risen, yet research on its impact on innovation processes between actual businesses remains sparse. This research fills the present gap by investigating ten workers from a tech startup who utilize artificial intelligence tools in operational and creative activities. The paper analyzes business-related AI functionality through a qualitative analysis of ten tech start-up employees. The examination reveals that AI produces significant enhancements in problem resolution by executing mundane actions while analyzing large datasets to deliver data-driven suggestions to users. The interview respondents mentioned that AI’s role in diminishing supply chains is 15%, while allowing AI to manage customer service without employee engagement in 80% of interactions. The implementation costs, along with data dependency and occasional contextual blindness in AI systems, represented some of the problems in this system. Analysis demonstrated that AI tools enable the development of innovative concepts and challenge established viewpoints, prompting participants to create a gamified loyalty system and dynamic content planning. Participants in the study emphasized the need for human involvement to refine AI-based insights, recognizing how human imagination complements AI capabilities effectively. The work enhances academic discussions about AI-related problem-solving and creativity while offering specific business-related recommendations for implementation. The recommendations begin with establishing initial experimental programs, while providing support for employee’s skills development, and fostering strong alliances between technical AI personnel and professional subject matter experts. Research topics focused on AI application fields and the anticipated impacts on company decision-making, as well as the ethical ramifications, need further exploration. This research confirms the revolutionary potential of artificial intelligence systems for problem-solving methods, but requires proper execution, along with human supervision, to fully realize their advantages. Full article

Spatial reasoning ability plays a critical role in predicting academic outcomes, particularly in STEM (science, technology, engineering, and mathematics) education. According to the Cattell–Horn–Carroll (CHC) theory of human intelligence, spatial reasoning is a general ability including various specific attributes. However, most spatial assessments focus on testing one specific spatial attribute or a limited set (e.g., visualization, rotation, etc.), rather than general spatial ability. To address this limitation, we created a mixed spatial test that includes mental rotation, object assembly, and isometric perception subtests to evaluate both general spatial ability and specific attributes. To understand the complex relationship between general spatial ability and mastery of specific attributes, we used a higher-order linear logistic model (HO-LLM), which is designed to simultaneously estimate high-order ability and sub-attributes. Additionally, this study compares four spatial ability classification frameworks using each to construct Q-matrices that define the relationships between test items and spatial reasoning attributes within the HO-LLM framework. Our findings indicate that HO-LLMs improve model fit and show distinct patterns of attribute mastery, highlighting which spatial attributes contribute most to general spatial ability. The results suggest that higher-order LLMs can offer a deeper and more interpretable assessment of spatial ability and support tailored training by identifying areas of strength and weakness in individual learners. Full article

Amid ongoing educational reforms and the rapid advancement of the knowledge economy, innovative teaching behaviors are not only closely related to teachers’ professional growth and students’ academic achievement but are also regarded as the key driving force for the evolution of the educational system. Consequently, identifying effective ways to foster teachers’ innovative teaching behaviors has become a central concern in educational psychology and management. Grounded in the Job Demands–Resources framework, this study developed and tested a chained mediation model using survey data from 1163 Chinese elementary and secondary school teachers. The model examines how teachers’ emotional competence fosters innovative teaching behaviors and elucidates the underlying mechanisms. The results revealed that (1) emotional competence significantly and positively predicted innovative teaching behaviors, and (2) teaching efficacy and work vitality served not only as independent mediators but also as sequential mediators in this relationship. These findings extend the understanding of the antecedents of teachers’ innovative behaviors from an emotional perspective, demonstrating that emotional competence, as a critical psychological resource, can be transformed into innovative teaching behaviors through dual “cognitive–motivational” and “energy–motivational” pathways. This study offers both theoretical insights and practical implications for advancing teaching innovation by strengthening teachers’ emotional competence, teaching efficacy, and work vitality. Full article

Research visibility has become a critical issue for universities, yet the institutional conditions that shape it remain underexplored. While Current Research Information Systems (CRISs) provide essential infrastructure for managing publications and researcher profiles, their impact depends on broader governance and cultural factors. This study compares four universities—two in Peru, one in Chile, and one in Spain—that have adopted the Pure CRIS platform. Data were manually extracted from institutional portals and analyzed descriptively, using normalized indicators such as publications per researcher, Sustainable Development Goal (SDG) alignment, and collaboration networks. Although based on a limited sample, the analysis highlights substantial contrasts: European institutions show consolidated integration of CRIS into national evaluation systems, while Latin American universities remain at earlier stages of adoption, with fragmented policies and limited international reach. The findings suggest that technological platforms alone are insufficient; institutional commitment, coherent policies, and academic cultures that value dissemination are decisive. These insights contribute a comparative framework to guide universities, particularly in Latin America, seeking to strengthen their global research visibility. Full article

This study presents a bibliometric and thematic analysis of economic convergence analysis from 1982 to 2025, based on a corpus of 2924 Scopus-indexed articles. Using VOSviewer and the bibliometrix R package, this research maps the field’s intellectual structure, identifying five main thematic clusters: (1) formal statistical models, (2) institutional-contextual approaches, (3) theoretical–statistical foundations, (4) nonlinear historical dynamics, and (5) normative and policy assessments. These reflect a shift from descriptive to explanatory and prescriptive frameworks, with growing integration of sustainability, spatial analysis, and institutional factors. The most productive journals include Journal of Econometrics (121 articles), Applied Economics (117), and Journal of Cleaner Production (81), while seminal contributions by Quah, Im et al., and Levin et al. anchor the co-citation network. International collaboration is significant, with 25.99% of publications involving cross-country co-authorship, particularly in European and North American networks. The field has grown at a compound annual rate of 14.4%, accelerating after 2000 and peaking in 2022–2024, indicating sustained academic interest. These findings highlight the maturation of convergence analysis as a multidisciplinary domain. Practically, this study underscores the value of composite indicators and spatial econometric models for monitoring regional, environmental, and technological convergence—offering policymakers tools for inclusive growth, climate resilience, and innovation strategies. Moreover, the emergence of clusters around sustainability and digital transformation reveals fertile ground for future research at the intersection of transitions in energy, digital, and institutional domains and sustainable development (a broader sense of structural change). Full article

The successful implementation of lane change and overtaking maneuvers, as well as the technological advancements in new energy vehicles in China, are outcomes influenced by multiple factors. Among these factors, the responsiveness of local policies plays a crucial role and serves as a pivotal element in ensuring the effective execution of central policies. Nevertheless, there is a dearth of systematic research within the academic community regarding the innovative impacts of local policy responses. We utilize industrial policy and patent data from China’s NEV sector, employing text analysis to measure local policy response in terms of intensity, velocity, and degree. Regression analysis is conducted to investigate the impact of local policy responses on technological innovation. The findings reveal an inverted U-shaped correlation between policy issuance frequency, adoption speed, policy reproduction degree, and technological innovation. Regional disparities play a moderating role in the local policy response impact, with the eastern region exhibiting superior policy response compared to the central and western regions. Notably, an inverted U-shaped relationship is observed between adoption speed and policy reproduction degree in the eastern region, as well as between policy issuance frequency in the central region and technological innovation. Conversely, no significant policy response effect is detected in the western region. These outcomes underscore the necessity for effective local policy response, emphasizing the need for local governments to adapt and customize central policies in alignment with local contexts while navigating the balance between central coherence and local diversity, as well as policy adjustments and temporal constraints. This article contributes to the existing literature on policy implementation and innovative governance, offering empirical insights to enhance the optimization of regionally tailored policy frameworks and to bolster the coherence and efficacy of central and local policies. Full article

This study examines how cognitive biases may shape ethical decision-making in AI-mediated environments, particularly within education and research. As AI tools increasingly influence human judgment, biases such as normalization, complacency, rationalization, and authority bias can lead to ethical lapses, including academic misconduct, uncritical reliance on AI-generated content, and acceptance of misinformation. To explore these dynamics, we developed an LLM-generated synthetic behavior estimation framework that modeled six decision-making scenarios with probabilistic representations of key cognitive biases. The scenarios addressed issues ranging from loss of human agency to biased evaluations and homogenization of thought. Statistical summaries of the synthetic dataset indicated that 71% of agents engaged in unethical behavior influenced by biases like normalization and complacency, 78% relied on AI outputs without scrutiny due to automation and authority biases, and misinformation was accepted in 65% of cases, largely driven by projection and authority biases. These statistics are descriptive of this synthetic dataset only and are not intended as inferential claims about real-world populations. The findings nevertheless suggest the potential value of targeted interventions—such as AI literacy programs, systematic bias audits, and equitable access to AI tools—to promote responsible AI use. As a proof-of-concept, the framework offers controlled exploratory insights, but all reported outcomes reflect text-based pattern generation by an LLM rather than observed human behavior. Future research should validate and extend these findings with longitudinal and field data. Full article

Prefabricated Construction (PC) Technology is recognized for its advantages in reducing carbon emissions, lowering energy consumption, conserving materials, and improving waste management. Despite significant research efforts, few systematic analyses have been conducted to consolidate the current understanding of carbon emissions in PC. To address this gap, the present study undertakes a comprehensive review using a synergistic approach that integrates scientometric and rigorous qualitative analyses. The aim is to synthesize state-of-the-art research on carbon emissions in PC and provide insightful directions for future academic work in this field. A database of 114 relevant journal articles was compiled through a meticulous data collection process, followed by scientometric analysis to map influential journals, key articles, active countries, and emerging research trends. The qualitative analysis identifies prevailing research domains, highlights critical research gaps, and anticipates future needs. This study contributes to enriching the existing knowledge base and offers both theoretical insights and practical guidance for advancing low-carbon construction, optimizing assessment frameworks, and promoting interdisciplinary collaboration and informed policymaking. Full article

Over the past decade, digitalization in the logistics sector has heightened the significance of autonomous systems and AI-based applications, while the integration of advanced deep learning technologies with air cargo carriers has ushered in a new era in the logistics industry. This study systematically addresses the current applications of these technological advances in logistics planning, the challenges faced, and perspectives for the future. These developments are transforming the role of UAVs and autonomous systems in logistics operations by improving last-mile efficiency and reducing costs. Key functions of autonomous vehicles, including environmental perception, decision-making, and route optimization, have shown notable progress through deep learning algorithms. However, major obstacles remain to their widespread adoption, particularly in terms of energy efficiency, data security, and the absence of a mature regulatory framework. Accordingly, this paper discusses these issues in detail and highlights areas for further research. This systematic literature review reveals the disruptive potential of AACV for the logistics industry and presents findings that can guide both academic inquiry and industrial practice. The results underscore that establishing a sustainable and efficient logistics ecosystem is essential in the context of these emerging technologies. Full article

Reflection skills are a key but challenging element in teacher training. Feedback on reflective writing assignments can improve reflection skills, but it is affected by challenges (high variability in judgments and time investment). AI-generated feedback offers many options. Therefore, the aim of this study was to examine the potential of AI-generated feedback compared to that provided by lecturers for developing reflective skills. A total of 93 randomly selected pre-service teachers (70% female) in a course at a German university wrote two reflections and received feedback from either lecturers or ChatGPT 4.0 based on the same prompts. Pre-service teachers’ written reflections were assessed, and an online questionnaire based on standard instruments was applied. Control variables included metacognitive learning strategies and reflection-related dispositions. Based on a linear mixed model, the main effects on reflective skills were identified for time ($\widehat{\beta }$ = 0.41, p = 0.003) and feedback condition ($\widehat{\beta }$ = −0.42, p = 0.032). Both forms of feedback similarly fostered reflective skills over time, with academic self-efficacy emerging as a pertinent disposition ($\widehat{\beta }$ = 0.25, p = 0.014). The limitations of this study and implications for teacher training are discussed. Full article

The integration of multimedia technologies is transforming teaching and learning in higher education, offering innovative ways to improve student engagement and learning outcomes. Although numerous studies investigate the impact of multimedia, there is still a clear need for a synthesis that brings together the latest evidence from a variety of disciplines and contexts. To address this need, this systematic review aims to summarize the empirical evidence and provide a clearer understanding of how multimedia is applied in higher education, to outline how educators can effectively design and the implications for curriculum design. This article focuses on three key research questions: (1) How does the integration of multimedia in higher education classrooms influence student engagement and learning outcomes? (2) How does the use of multimedia affect the development of specific skills? (3) What are the challenges and opportunities to integrate multimedia technologies into higher education? Relevant studies were systematically retrieved and screened from major academic databases, including ScienceDirect, Web of Science, IEEE Xplore, Wiley Online Library, Springer, Taylor & Francis, and Google Scholar. In total, 48 studies were selected from these sources for detailed analysis. The findings showed that multimedia tools enhance student engagement, motivation, and performance when integrated with clear pedagogical strategies. In addition, multimedia helps to develop skills such as creativity, digital literacy, and independent learning. However, challenges such as technical limitations, uneven infrastructure, and the need for ongoing teacher training remain significant difficulties in fully exploiting the benefits in higher education. Addressing these challenges requires coordinated institutional support, investment in professional development, and careful alignment of multimedia tools with pedagogical goals. Full article