Search Results (8,988)

Generative Artificial Intelligence (GenAI) has the potential to bring substantial benefits to language education, making it essential to examine how teachers engage with these technologies in practice. This exploratory qualitative case study draws on semi-structured interviews with four in-service upper-secondary English teachers in Norway to examine the factors shaping their engagement with GenAI. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT), the study examined factors shaping teachers’ engagement with GenAI, including performance expectancy, effort expectancy, social influence, and facilitating conditions. Thematic analysis revealed a pattern of selective, context-sensitive use rather than straightforward adoption. While teachers recognised the potential of GenAI to support planning, idea generation, and formative feedback, their engagement was constrained by concerns about assessment validity, academic integrity, privacy, and institutional guidance. The findings suggest that teachers’ use of GenAI is shaped not only by perceptions of usefulness and ease of use but also by trust, assessment considerations, and the availability of clear policy frameworks. By using UTAUT as a qualitative analytical lens, this study contributes to research on technology acceptance and teacher agency by showing how teachers negotiate the use of GenAI in ways that reshape assessment practices and professional roles. The findings point to the need for clear institutional guidance, AI-resilient assessment practices, and targeted teacher education that supports ethical, pedagogically grounded use of GenAI. Full article

The integration of artificial intelligence (AI) into early childhood education presents both opportunities and challenges to longstanding Froebelian pedagogies, particularly regarding child agency and nature-based play. This mixed-methods study explores this tension within the Chinese context. It examines how Chinese Froebelian practitioners perceive the alignment between AI tools and core principles and investigates the strategies they employ to navigate the integration of technology with humanistic educational values. The survey results, from 50 practitioners, revealed that AI can support autonomous and holistic learning, yet significant concerns persisted regarding the displacement of sensory and nature-based experiences. Follow-up interviews uncovered a practitioner-led “dual-track integration” approach, which strategically blends physical manipulation and nature engagement with AI-enabled personalization. Through an iterative dialogue between theory and data, this study develops and refines the “dual-track integration” framework as an empirically grounded, sensitizing model. This framework offers principled strategies for hybrid learning that uphold the developmental primacy of play. Situated within the discourse on Sustainable Development Goal 4 (quality education) and Goal 10 (reduced inequalities), the analysis highlights AI’s dual potential to advance or hinder equity. By examining China’s hybrid position, which combines advanced digital infrastructure with persistent equity gaps, this research highlights the critical role of educator agency and pedagogical design in leveraging AI to advance equitable, high-quality early childhood education. Full article

Critical thinking dispositions are essential motivational drivers for intellectual excellence; yet their relationship with socio-emotional traits, such as humour, remains under-researched. This study investigated associations between critical thinking dispositions and the four humour styles (Affiliative, Self-enhancing, Aggressive, Self-defeating) in higher education, controlling for gender, field of study, and academic year. A quantitative, correlational design was used with 382 Portuguese university students who completed the Critical Thinking Dispositions Scale and the Humour Styles Questionnaire. Our results showed that Open-mindedness predicted Affiliative humour, while CT Self-confidence and Cognitive maturity predicted Self-enhancing humour. Truth-seeking inversely predicted Aggressive humour, which was higher in males and students of Science and Technology. Self-defeating humour was uniquely predicted by lower Cognitive maturity. These findings underscore that adaptive humour aligns with reflective thinking, whereas maladaptive styles correlate with traits that may hinder epistemic engagement. These findings underscore that adaptive humour is associated with reflective thinking, whereas maladaptive humour styles correlate with dispositional traits that may impede epistemic engagement; taken together, the results highlight the importance of integrating educational strategies that foster critical thinking dispositions, as such strategies may facilitate the development of more adaptive humour styles. Full article

This study addresses the growing vulnerability of societies to extreme weather events intensified by climate change and explores how Secondary Education can foster sustainability competences aligned with the European GreenComp framework. A mixed-methods design was used, combining a content analysis of 279 curricular units from educational legislation and STEM subjects in Compulsory Secondary Education and Baccalaureate, a questionnaire administered to 190 students, and the design and classroom implementation of a GreenComp-based teaching intervention. The curricular analysis revealed uneven integration of sustainability competences across STEM disciplines, with stronger presence in Biology, Geology and Technology, and limited representation in Mathematics and Physics and Chemistry. Student perceptions showed fragmented understandings of extreme weather events, their causes and consequences, and limited awareness of global frameworks such as the SDGs and COP meetings. The implemented teaching sequence improved students’ knowledge of extreme events, strengthened their recognition of links with climate change, and increased awareness of mitigation, adaptation, and the role of education and political action. Overall, the findings highlight both opportunities and gaps in current curricula and demonstrate the potential of contextualized, inquiry-based STEM approaches to develop sustainability competences and better prepare students to face extreme weather events. Full article

Against the backdrop of intensified global talent competition, the return of overseas talents has become a key engine driving the enhancement of core competitiveness in developing countries. Accurately identifying its critical driving factors is essential for China to address the challenges of talent introduction. This study constructs a hybrid multiple-criteria decision-making framework to systematically explore the influence mechanism of overseas talent return: first, a 15-criterion decision structure covering economic, policy, educational, technological, and social aspects is established via systematic literature review and two-round Delphi expert surveys; second, the grey DEMATEL-ANP technique is adopted to quantify the inter-relationships and relative weights of the criteria and screen and rank the critical driving factors accurately. Empirical results show that the six core driving factors ranked by importance are talent policy support, economic development level, scientific and technological development strength, public service quality, educational resource supply, and attention to science and technology, with significant synergistic interaction effects among these factors. This research provides a scientific decision-making framework and empirical support for developing countries to formulate targeted talent introduction policies and optimize the talent development ecosystem. Full article

Sustainable regional competitiveness is widely recognized as a cornerstone for fostering economic growth, social well-being, and environmental sustainability at the local level. Building upon our previous research, in which we extensively examined the ten factors shaping regional competitiveness, this study continues the investigation by focusing on the same nine factors while replacing environmental considerations with civil protection, utilizing updated literature spanning 2020 to 2025. The study’s time frame was from March 2025 to November 2025. A literature review methodology was adopted, emphasizing critical evaluation rather than a systematic review. Recent studies published within the last five years were analyzed, with particular attention to these ten recognized factors: (1) economy, (2) labor market, (3) poverty and social inclusion, (4) healthcare, (5) educational infrastructure, (6) environmental considerations, (7) transportation infrastructure, (8) science and technology, (9) high-tech industries, and (10) innovation. The key findings of the study emphasize the distinct yet interconnected role of each factor in shaping regional competitiveness. Economic development remains foundational, closely linked with education, causes of death, and sustainability, highlighting that a strong economy alone is insufficient. Labor market dynamics, including youth employment and skills development, are crucial for translating potential into growth, while addressing poverty and social exclusion requires coordinated social and economic policies. Public health indicator reflect societal challenges and helps identify areas where targeted interventions can enhance well-being and productivity. Education strengthens human capital, supports innovation and high-tech industries, and promotes social inclusion, creating the foundation for sustainable regional growth. Environmental issues shape the risks that civil protection must manage, while effective environmental protection reduces the need for emergency response. Transportation infrastructure connects economic activity, Research & Development (R&D), Information and Communication Technologies (ICT) deployment, and innovation, enhancing regional integration. Science and technology, particularly ICT, drive productivity and competitiveness, while human capital plays a central role in the development of high-tech industries, supporting innovation and economic diversification. Finally, innovation underpins the capacity of regions to adapt and maintain a long-term competitive advantage. Overall, this research demonstrates that by retaining the same nine core factors and replacing environmental considerations with civil protection, it is possible to gain new insights into regional competitiveness. Full article

Generative Artificial Intelligence (GAI) is rapidly reshaping pedagogical practices and offering new opportunities to advance sustainability within higher education. This study investigates the extent to which postgraduate students utilize GAI to support Sustainable and Environmentally Responsible Teaching Practices (SERTPs), and examines whether this use varies across demographic, academic, and technological characteristics. A descriptive quantitative design was employed, involving 310 postgraduate students from the College of Education at King Khalid University. Data were collected using a validated and highly reliable instrument measuring five dimensions of GAI-supported sustainable teaching. Descriptive and inferential analyses, including t tests, one-way ANOVA, and LSD post hoc comparisons, were conducted. The findings reveal that postgraduate students demonstrate a moderate overall level of GAI use in advancing SERTPs, with the highest engagement occurring in the promotion of sustainable educational practices. Significant differences were only found in relation to students’ levels of technology use and students’ levels of GAI use, indicating that frequent and sophisticated engagement with AI tools is the strongest predictor of sustainable teaching practices. No significant differences emerged across gender, age, academic department, program level, or specialization. The study highlights the need for targeted training and institutional strategies that enhance students’ AI proficiency, thereby enabling GAI to serve as a catalyst for environmentally responsible and sustainable teaching practices in higher education. Full article

Extended Reality (XR) technologies are increasingly used to create immersive and interactive systems across domains such as education, training, health, and entertainment. As these systems become more complex and multisensory, evaluating user experience (UX) in XR environments requires approaches that go beyond traditional usability assessments and consider perceptual, cognitive, emotional, and interaction-related factors. However, existing UX evaluation efforts in XR often rely on isolated instruments or domain-specific studies, lacking a systematic and reusable evaluation methodology. This paper proposes a human-centered methodology for evaluating user experience in extended reality applications, integrating UX dimensions and XR-specific characteristics into a structured and coherent evaluation process. The methodology is grounded in a multi-phase research process that includes a comprehensive literature review, expert consultation, correlation analysis between UX dimensions and XR features, and formal specification of evaluation phases and activities. Based on this process, the proposed methodology supports evaluators in selecting appropriate UX evaluation methods and instruments according to the characteristics and experiential goals of XR applications. The methodology defines a set of UX dimensions tailored to immersive environments, capturing perceptual, cognitive, emotional, and interaction aspects that are critical for the design and evaluation of adaptive and human-centered XR systems. An expert-based validation was conducted to assess the clarity, usefulness, and applicability of the methodology, leading to refinements in its structure and descriptions. The methodology promotes a human-centered approach by considering user perception, emotional impact, and contextual experience across XR modalities. It additionally contributes to the field by offering a reusable process for UX evaluation in XR, supporting more consistent, transparent, and human-centered assessment practices. It also provides a foundation for future empirical studies and the development of evaluation approaches inspired by natural and adaptive human–environment interactions. Full article

In arid-zone water resource management and water-security assessment, changes in water-body volume are key indicators of water availability and regulation performance. However, arid-zone lakes often lack sufficient bathymetric information to constrain geometry under low lake-level conditions. Shrinkage-driven hydrological disconnection can destabilize extrapolation of water level–storage relationships. This increases uncertainty in quantifying long-term storage changes. Here, we develop a multi-digital elevation model (DEM) hypsometry framework to reconstruct near-monthly lake storage for 1993–2024, recovering storage during low-level periods without bathymetric surveys. Reconstructed changes agree with independent satellite altimetry (r = 0.93 for level and 0.90 for storage), outperforming above-water-only (r ≈ 0.637 for water level) and conventional model-selection base-lines (r ≈ 0.753 for water level). The framework was quantified across three scenarios: expanding lakes, lake systems and reservoirs, and terminally shrinking lakes. For the persistently shrinking Big Aral Sea, under the whole-lake modeling assumption, the Copernicus-based reconstruction provides a cumulative storage change of −214.3 km³, closest to the satellite altimetry estimate of −210.68 km³ among the tested DEMs. In contrast, other DEMs overestimate the 1993–2024 cumulative loss by 66.15–141.01 km³. Sub-lake modeling further adjusts the Shuttle Radar Topography Mission (SRTM)-based cumulative change to −248.38 km³, substantially reducing structural bias caused by lake disconnection. This study provides a transferable technical framework for lake storage reconstruction in arid regions under degraded low lake-level conditions and hydrological disconnection. Full article

Background/Objectives: The rapid expansion of generative artificial intelligence (AI) and large language model-based chatbots has accelerated their adoption in higher education, including nursing. This scoping review mapped the use of AI-based chatbots in nursing education, including curricular domains, pedagogical approaches, educational outcomes, and implementation challenges. Methods: A scoping review was conducted following the Joanna Briggs Institute methodology and reported in accordance with the PRISMA-ScR guideline. Searches were performed across major bibliographic databases and grey literature sources. Quantitative, qualitative, and mixed-methods studies addressing the use of AI chatbots in nursing education or professional training were included. Data were extracted using a standardized instrument and synthesized through descriptive statistics and qualitative content analysis. Results: Sixty-six studies (2019–2025) were included, with significant growth observed after 2023. Most studies employed quasi-experimental designs (37.9%) and were implemented in academic settings (83.3%). Application formats varied across online, hybrid, simulation-based, and classroom models. Reported benefits included improved learning performance, clinical reasoning, and student engagement. Key challenges involved the reliability of AI outputs, academic integrity, data protection, and limited institutional governance. Conclusions: AI-based chatbots represent promising tools to enhance nursing education, particularly when integrated into structured pedagogical strategies with active faculty supervision. Their use can support the development of clinical reasoning, student engagement, and personalized learning. However, methodological heterogeneity, ethical concerns, and governance gaps highlight the need for careful implementation and further rigorous research to ensure safe, effective, and pedagogically sound integration. Full article

Artificial Intelligence (AI) has emerged as a transformative force, increasingly integrated into diverse aspects of modern society, from healthcare and education to business and entertainment. Among the most influential AI technologies are large language models (LLMs), such as generative pretrained transformers (GPTs). These models are designed to process vast amounts of data and perform complex computations, enabling advanced capabilities in natural language understanding and generation. However, deployment and operation of such systems requires significant computational resources, leading to substantial energy consumption. While general-purpose hardware such as GPUs is limited by fixed-precision architectures, field-programmable gate arrays (FPGAs) offer the bit-level reconfigurability needed to exploit ultra-low-bitwidth representations. This allows power-intensive multiplications to be replaced by streamlined logic-based accumulations, maximizing the energy benefits of model quantization. This paper addresses the problem of the energy impact of LLMs by leveraging innovative FPGA-based heterogeneous computing platforms. Results demonstrate that ternary matrix multiplication (MatMul) achieves a 23% speedup and a remarkable 96% reduction in digital signal processor (DSP) utilization. Furthermore, the final optimized design shows a 52% reduction in total energy consumption compared to the baseline, making heterogeneous computing a compelling solution for power- and resource-constrained embedded applications. Full article

Given the persistent challenges in promoting pro-environmental behavior and student engagement in higher education, particularly in environmental courses, this study examines the effects of creative teaching strategies—specifically icebreaker games and activities—on cognitive understanding, attitudes, and pro-environmental behaviors among first-year university students in environmental education. Grounded in the Green Competency framework and game-based learning theory, the study addresses an empirical gap concerning the sustained impacts of active learning approaches. A Solomon four-group experimental design was employed with 200 students enrolled in the Environmental Society course at Rajamangala University of Technology Thanyaburi (RMUTT). Pre- and post-tests assessed changes across the three learning domains. ANOVA and Scheffé post hoc analyses revealed statistically significant improvements in cognition, attitudes, and behaviors among students exposed to the intervention, particularly those receiving both pre-testing and innovative instruction. Regression analysis indicated that cognitive understanding was the strongest predictor of pro-environmental behavior (β = 0.531, p < 0.001), while demographic variables showed no significant influence. The findings demonstrate that well-designed icebreaker activities can enhance student engagement and foster lasting behavioral change when aligned with course objectives. This study contributes to the sustainability education literature by linking active pedagogy, emotional engagement, and behavioral outcomes and offers practical implications for student-centered curriculum design in higher education. Full article

Computational thinking (CT) is increasingly being integrated into educational curricula alongside mathematical thinking (MT) within science, technology, engineering, and mathematics (STEM) education. Physical computing devices now support low-code programming approaches aligned with Sustainable Development Goal 4 (Quality Education) by helping to create engaging and inclusive learning environments for learners, particularly P–12 students and their teachers. However, the use of such devices for low-code programming remains underexplored and insufficiently evaluated. This study investigates the application of low-code programming using a specific physical computing device, the micro:bit, within a gamified context to foster perceive readiness for CT in sustainable mathematics education for P–12 students, while also considering the perspectives of pre-service teachers (PSTs). PSTs often lack adequate preparation to teach related concepts and to manage the affective dimensions that influence learning. Findings indicate that positive emotions increased and negative emotions decreased, except for frustration and boredom, following the intervention. Additionally, interest in and engagement with the development perceive readiness for CT and MT improved among PSTs within a sustainable (STEA)Mathematics education framework. These results suggest that the proposed approach helps address existing gaps and may be adapted across diverse academic and professional domains, supporting continuous knowledge acquisition under both predictable and uncertain conditions. Full article

Green building is a practical pathway for meeting the European Green Deal objectives through lower life cycle impacts, healthier indoor environments, responsible material use, and improved resource efficiency across construction and renovation. This paper develops and characterises a competence framework for green building derived from the GreenSCENT competence framework materials. The framework is organised into four competence areas and twelve competences, each articulated through sets of knowledge, skills, and attitudes and mapped across European Qualifications Framework levels. The resulting framework contains 276 statements distributed across knowledge, skills, and attitudes, enabling curriculum design, formative assessment, and micro credential development for learners ranging from introductory to expert levels. Quantitative profiling highlights uneven density across competences, with project management and energy saving in buildings carrying the largest statement sets, indicating strong cross cutting requirements in governance and operational performance. The framework supports education and training that connects building design, material stewardship, technology selection, circular practices, and economic decision, making in a single competence logic aligned with Green Deal policy directions. Full article