Search Results (669)

Across four decades of AI deployment, the same six human challenges (trust calibration, reliance behavior, cognitive engagement, skill retention, accountability, and transparency) recur, yet fragmentation across research communities obscures this continuity and limits knowledge transfer. Functionally similar phenomena are repeatedly relabeled (a jangle fallacy): what aviation researchers call “automation complacency,” decision scientists call “algorithm appreciation,” and LLM researchers describe as “over-reliance.” This systematic review synthesizes 152 papers spanning aviation, healthcare, manufacturing/supply chain, and cross-domain contexts across three AI technology generations: decision support systems, autonomous systems, and large language model (LLM) agents. We introduce the Collaboration Convergence Framework (CCF), a 6 × 3 matrix with solution-maturity indicators that maps each challenge across generations. The framework shows that Gen 3 designers can transfer decades of evidence from automation and decision support research (particularly reliance calibration, cognitive forcing, and skill maintenance) rather than rediscovering them. Cross-generational synthesis also isolates three Gen 3 phenomena without direct precedent in earlier generations: epistemia (attributing genuine knowledge to LLMs based on surface fluency), attribution ambiguity in co-creation, and motivational withdrawal. We distill twelve transferable design principles and propose ten research directions, prioritizing skill-retention interventions and accountability frameworks. These findings carry direct sustainability implications aligned with Industry 5.0: protecting workforce capability under increasing automation (SDG 8), reducing duplicated research effort through cross-generational knowledge reuse (SDG 9), and supporting responsible deployment by treating collaboration risks as predictable rather than novel (SDG 12). The CCF provides conceptual infrastructure for cumulative learning across AI generations and industries. Full article

Developing translation competence among non-English-major students at Chinese universities remains a pedagogical challenge, especially given the rising demands of cross-cultural communication. This quasi-experimental study examined whether first-year medical students at a Chinese university could improve their translation skills using the constructivist–objectivist theoretical approach (COTA), which combines constructivist learning theories (e.g., active student participation, collaboration, analysis of real-world issues) with objectivist learning methodologies (e.g., sequential skill development, explicit knowledge transfer). In total, 110 students participated in this mixed-methods study. The research methods included (a) pre- and post-tests of students using College English Test Band 4 criteria to evaluate vocabulary, grammar, and accuracy; (b) student perception surveys; (c) semi-structured interviews with instructors; and (d) classroom observations of students, using Gagné’s nine instructional events to ensure faithful implementation of the COTA framework. The COTA-trained students showed statistically significant improvements in translation skills compared to the control group. Additionally, increased student participation and engagement, positive attitudes toward learning, instructors’ ability to implement COTA effectively, and areas for future development were identified in the qualitative findings. These results suggest that integrating constructivist and objectivist teaching philosophies can benefit curriculum designers, language and translation instructors, and policymakers aiming to enhance translation education in Chinese universities and other Asia-Pacific institutions. However, the modest sample size from a single institution limits generalizability, and future studies with larger, more diverse samples are recommended. Full article

Enhancing spatial orientation and map-reading skills is a cornerstone of geography education, yet the comparative efficacy of physical versus virtual reality learning environments (VRLEs) remains a subject of ongoing debate. This study evaluates the development of navigational competencies through a counterbalanced crossover experimental design involving 20 geography and geography teacher major students. Participants performed standardized spatial tasks, including bearing calculation and distance estimation, in both the volcanic landscape of the Tapolca Basin, Hungary, and its smartphone-based 360-degree virtual reality (VR) counterpart. To assess longitudinal retention and cross-modal transfer, a three-month interval was maintained between the two learning phases, supported by a robust pre-test/post-test framework. Results indicate that while both environments are susceptible to spatial distortions driven by the visual dominance of physiographic landmarks, VR-based training effectively scaffolds the cognitive frameworks required for real-world navigation. The findings confirm that spatial mental models acquired in a virtual setting possess significant cognitive resilience, as navigational accuracy was maintained over the three-month interval. In conclusion, this research justifies a hybrid pedagogical approach, where immersive digital simulations serve as a preparatory tool for physical fieldwork. The synergy of both modalities is essential for cultivating the resilient spatial intelligence required for professional geographic practice. Full article

Accurate in-season crop type mapping is critical for agricultural monitoring and yield assessment, yet most operational products remain proprietary, post-seasonal or insufficiently tested across contrasting seasons. This study presents an open and transferable framework that quantifies how in-season crop classification skills evolve through the growing season across the southwest agricultural region of Western Australia (WA) using a multi-temporal (2020–2024) Sentinel-2 derived vegetation indices (VIs) time-series. Six crop classes (i.e., wheat, barley, canola, lupins, pasture, and fallow) were evaluated using extreme gradient boosting (XGBoost) and long short-term memory (LSTM) models under a leave-one-year-out cross-validation (LOYOCV) design. Classification performance increased progressively through the season, with a marked improvement in late winter (late August to early September). In LOYOCV, overall agreement with the reference dataset exceeded 90% once vegetation-index observations through August were included, indicating that reliable in-season mapping was achievable before harvest. Canola was separated consistently from mid-season onwards, whereas reliable discrimination between wheat and barley required later phenological information. Independent field-based testing was used to assess true crop identification accuracy for the three externally observed classes: wheat, barley, and canola. In this test set, precision was highest for canola (0.93), followed by wheat (0.82) and barley (0.71). These field-based results supported the main temporal pattern observed in the LOYOCV analysis, particularly the strong mid-season separability of canola and the persistent confusion between wheat and barley. SHapley Additive exPlanations (SHAP) showed thatVIs centred on late winter contributed most strongly to model predictions, consistent with peak phenological divergence among crop types. These results identify a phenologically meaningful decision window for in-season crop mapping and provide a multi-year benchmark for evaluating temporal transferability in Mediterranean broadacre systems. Full article

Urban farming microinterventions are small, place-based cultivation projects that operate under severe spatial and resource constraints yet can generate social learning and locally embedded resilience. The present paper examines how design decisions shape the effectiveness of such interventions through three design-led case studies: Blooming Structure (2018, Warsaw), a temporary hydroponic “laboratory” installation; Micro-cultivation (2018, Warsaw), a shopfront vertical demonstration farm; and Micro-cultivation 2 (2019), modular “cultivation furniture” for interiors and exhibition deployment. The analysis combines project documentation with practice-based observations and applies five interpretive dimensions: spatial fit, technical feasibility, communicative legibility, replicability, and social programming. Findings highlight that successful microinterventions align legible cultivation infrastructure with high visibility, accessibility and participatory formats that support skills transfer and copying-based scaling. Rather than offering universal claims about urban agriculture outcomes, the paper provides a reference set of design principles that may inform similar micro-scale interventions in other contexts, subject to local constraints. Limitations include the small sample size and the concentration on projects from Poland. Practically, the findings can support designers, municipalities, and civic organisations in structuring microinterventions as replicable, low-threshold prototypes and in aligning technical systems with maintenance capacity and public engagement. Full article

This scoping review maps 224 empirical studies (205 from a structured Scopus search, 2020–2026, plus 19 from a targeted Central Asia supplement) across four digital technology domains for crop production: IoT and sensor-based systems, UAVs and remote sensing, machine learning and AI, and nanostructured agrochemicals. The review follows the PRISMA-ScR framework and pursues three research questions concerning documented effects and validation limitations (RQ1); cross-cutting barriers in human capital, data governance, and infrastructure (RQ2); and the state of empirical evidence from Central Asia and Kazakhstan relative to international findings (RQ3). Across all four domains, the strongest reported effects occur where the data-to-decision-to-action loop is closed and sustained over multiple seasons, yet most published metrics rest on single-season, single-site, or controlled-environment validation that overstates likely field portability. IoT and selected UAV and ML workflows are closest to operational readiness where maintenance, calibration, and advisory support are sustained. Nanostructured materials remain the least mature domain in agronomic terms. For Central Asia, foundational monitoring and salinity-oriented remote sensing are the most immediately transferable elements; intervention-grade ML and integrated digital systems require local calibration, extension infrastructure, and multi-season field validation that are largely still absent. The review identifies the digital skills gap, incomplete data governance, and underreported total cost of ownership as the principal institutional barriers to scaling. Policy priorities include shifting from technical pilots to multi-season agronomic proof, building intermediary service capacity, and establishing transparent data-governance frameworks before large-scale procurement. Full article

Human motion provides a valuable source of information for robotic skill acquisition, and Learning from Demonstration (LfD) has been widely adopted as an intuitive paradigm for enabling robots to learn tasks from human demonstrations. However, the lack of an explicit representation of transferable motion knowledge significantly limits the adaptability of LfD when tasks involve varying spatial constraints or environmental configurations. To address this challenge, this paper proposes a motion representation framework based on two fundamental properties of motion and introduces a novel Motion Knowledge Transferring Gaussian Mixture Model (MKT-GMM) for trajectory generalization across related tasks. In the proposed framework, demonstration trajectories from a source task are first collected through kinesthetic teaching and encoded using a Gaussian Mixture Model (GMM), where each Gaussian component represents a local motion primitive. Transferable motion knowledge is captured by jointly preserving the statistical characteristics of individual motion primitives and the geometric relationships between adjacent primitives. For a target task in which only task constraints are specified, the learned motion knowledge is transferred by adapting the GMM parameters through affine transformations combined with constraint-error minimization, enabling feasible trajectories to be generated without additional demonstrations or model retraining. The final motions are reconstructed using Gaussian Mixture Regression (GMR), ensuring smooth and consistent trajectory generation. To further improve the robustness of trajectory transfer, a pseudo via-point mechanism is introduced to automatically generate intermediate constraints when explicit via-points are unavailable. Experiments conducted on a robotic manipulation platform, including handwriting motion learning and pick-and-place tasks under varying task configurations, demonstrate that the proposed method effectively captures transferable motion knowledge and achieves reliable trajectory generalization for previously unseen tasks. Full article

Auditory neurofeedback training (NFT) based on brain–computer interfaces (BCIs) has recently entered the precision motor domain as a task-embedded neural state regulation paradigm. Compared to traditional standalone NFT approaches (e.g., relaxation or attention training designed to enhance general cognitive abilities), task-embedded paradigms integrate feedback directly into the motor task execution process. However, this design inevitably creates a dual-task scenario, and the effects of such a scenario on neural activity and behavioral performance have received limited systematic investigation in the existing literature. This study designed and implemented a closed-loop BCI system employing five-level heartbeat sound feedback and used this system as a research platform to examine the immediate neural mechanism changes and potential dual-task interference effects induced by single-session auditory NFT in moderately skilled shooters. The system maps real-time EEG features onto graded auditory signals varying in playback rate and volume intensity, incorporating a dynamic threshold adjustment mechanism. Twenty-two moderately skilled shooters completed three within-subject conditions (no-sound baseline, SMR enhancement, and theta suppression) in a single session with 32-channel EEG and behavioral data recorded simultaneously. Analyses employed whole-brain cluster-based permutation tests, cross-frequency coupling analysis, and functional connectivity analysis. Cluster-based permutation tests revealed that theta feedback induced a significant frontal 4–7 Hz suppression cluster (cluster p = 0.004), whereas SMR feedback did not produce significant 12–15 Hz enhancement at the group level. Theta feedback elicited cross-frequency spillover as follows: sensorimotor SMR power decreased significantly in theta responders (d = −0.69), with frontal theta and sensorimotor SMR changes positively correlated (r = 0.67, p < 0.001). Functional connectivity analysis using debiased weighted phase lag index (dwPLI) further demonstrated significant theta-band network reorganization (cluster p = 0.034). At the neural level, clear modulation effects were observed, but shooting ring values did not improve significantly under feedback conditions, and aiming time was significantly prolonged—a behavioral pattern consistent with potential dual-task interference from task-embedded auditory feedback. Single-session auditory NFT can act on the prefrontal cognitive control network and induce cross-frequency network reorganization, but the feedback channel itself constitutes a parallel task that may limit the short-term transfer of induced neural states to behavioral performance. This study examined the neural mechanisms of task-embedded auditory NFT and reported the dual-task costs that have been less characterized in prior “task + feedback” research, providing design considerations and preliminary mechanistic evidence for future development of auditory NFT in precision motor skill training. Full article

Roll forming is a sheet metal forming process characterised by high production rates and high material utilisation. Process-inherent inhomogeneous longitudinal strain distribution across the profiles cross-section requires the use of straighteners to maintain product quality within specifications during manufacturing. The straightening process is adjusted iteratively based on the expertise of line operators. A shortage of skilled workers leads to a loss of experience-based know-how in roll forming operations. To mitigate the effects on process productivity, machine learning (ML) can be applied to provide assistance to less experienced operators. In this context, force and position signals are recorded on a sensorically equipped straightener in order to predict corrective adjustments for line operators using convolutional neural networks. Furthermore, the integration of numerically generated data is investigated to reduce the required amount of labelled experimental data. Applying a Transfer Learning (TL) approach, the incorporation of numerical data reduces the mean error by 20.81% and the mean standard deviation by 36.62% for small experimental datasets. Full article

This article proposes an expanded framework of Professional Noticing (PN) for Sustainability in Higher Education by integrating intermedial semiotics and ecopoetry as pedagogical tools aligned with the United Nations Sustainable Development Goals (SDGs). Building on the PROMISE project, in which the author participated, the study conceptualises ‘noticing’ as an embodied, multimodal, and ethically inflected process of attending to human and more-than-human sign systems. The article introduces Intermedial Sustainability Noticing (ISN) as an extension of PN that foregrounds ecological awareness, intermedial perception, and cross-cultural interpretation. The study adopts a qualitative case study design based on the implementation of ISN within the Eurasia Foundation Cross-Cultural Partnerships hybrid course at Complutense University of Madrid. Participants included undergraduate students from diverse European and Asian institutions, who engaged in interdisciplinary and intercultural dialogues on sustainability through comparative literature and ecopoetry. In the course, students developed perceptual, interpretive, and ethical awareness of global challenges by emphasising ‘noticing’ and attentional depth while broadening understanding of ecological interdependence. Data were collected through reflective journals, written assignments, multimodal projects, and classroom discussions, and analysed using an interpretive, semiotically informed approach. Findings indicate that ISN fosters enhanced attentional depth, multimodal interpretive skills, and increased ecological awareness, particularly through structured engagement with ecopoetry. The work of Kathleen Jamie is presented here as exemplary of how literary texts can activate perceptual, interpretive, and responsive dimensions of noticing, enabling students to connect textual analysis with sustainability concerns. The article argues that ISN offers a transferable pedagogical model for embedding sustainability competencies within humanities curricula, contributing to Higher Education’s role in fostering ecological literacy, intercultural dialogue, and ethically grounded engagement with global challenges. Full article

Knowledge management (KM) is increasingly becoming a critical success factor in Germany’s manufacturing industry due to demographic change, the shortage of a skilled workforce, and the growing need for flexible and resilient production systems. This study contributes empirical evidence on current KM practices in manufacturing and derives practice-oriented design implications for future LLM-supported KM systems. Two consecutive survey rounds involving six companies in Survey 1 and five companies in Survey 2 were conducted in order to identify current KM practices, recurring barriers, and design implications for large language model (LLM)-supported KM. The results show that KM is perceived as highly relevant, but is implemented only incompletely in practice. Across both datasets, central themes such as fragmented documentation practices, reliance on interpersonal transfer of tacit knowledge and uneven integration of digital KM tools recur consistently. Based on the identified practices, the paper further derives areas in which LLMs may support or augment existing KM processes, particularly with regard to semantic retrieval, contextualization, onboarding, and the preservation of tacit knowledge. The findings also highlight that successful implementation of artificial intelligence (AI)-enabled KM in manufacturing will depend on technical feasibility, trust, usability, and organizational acceptance. Full article

Understanding the role of historical lightning-driven fire regimes in shaping terrestrial ecosystems and carbon cycles requires reconstructing fire from data beyond the instrumental record. Previous efforts have relied on paleo proxies, such as charcoal records, but these approaches are limited by their coarse spatial extent. Alternatively, process-based modeling offers a spatially continuous pathway for simulating lightning-caused fire regimes. However, existing lightning prediction models use upper-atmospheric variables, such as convective available potential energy (CAPE), that are not available in paleoclimate reconstructions, limiting their use beyond the instrumental period. Here, we develop a probabilistic framework for simulating lightning-caused fire ignitions that (1) relies on variables available in paleo reconstructions (near-surface climate, fuel moisture, and land cover) and (2) decomposes lightning-caused fire occurrence into two components: lightning strike rate and lightning ignition efficiency. Both components were trained on modern observational data for Alaska during 2002–2011, and then combined in a Bernoulli model to estimate daily fire probability. Near-surface climate predictors captured spatial and temporal variability in lightning activity with performance comparable to CAPE-based models, and ignition efficiency models showed strong discrimination between fire-causing and non-fire-causing strikes. Despite overestimation under high-risk conditions, the Bernoulli model demonstrated strong discriminatory skill (ROC AUC = 0.894), effectively ranking fire risk across space and time. By explicitly separating lightning occurrence from ignition efficiency and relying on variables available in paleo reconstructions, this approach provides a transferable framework for simulations of historical lightning-fire regimes. Full article

While agricultural modernization improves productivity, it may worsen rural inequality. Without systematic guidance and institutional rules, it harms inclusive and sustainable rural development. To examine the income distribution effects of two distinct modernization pathways, this study uses an innovative dual-mode framework integrating resource endowment, mechanism, and distribution to compare Land Transfer and Non-farm Employment. Based on a survey of 963 farm households in modern agricultural parks of Sichuan Province, we apply regression, endogeneity correction, mechanism and heterogeneity analysis. The study found that Land Transfer exhibits a significant positive correlation with income growth through economies of scale and labor release effects, yet its benefits primarily flow to local elite groups with superior resource endowments, demonstrating an “elite capture” tendency; Non-farm Employment is closely linked to income growth by raising wage levels, enhancing skill levels, and improving employment stability. Its benefits are more likely to reach ordinary, low-income, and less-educated farmers, reflecting the characteristic of “inclusive growth.” The framework reveals divergent equity outcomes of efficiency-oriented reforms, providing new insights for building fair and sustainable agricultural systems. It also provides micro-level policy references for SDG 10 (reduced inequalities) and SDG 8 (decent work and economic growth). Full article

Background: Empathy is a critical component of patient-centered care, influencing clinical outcomes, patient satisfaction, and adherence. Despite its recognized importance, empathy often declines during medical training, particularly in clinical years. Traditional teaching methods, such as reflective writing or self-assessment scales, often fail to promote sustained behavioral change. This study evaluated a structured, skills-focused empathy training program integrated into the third-year medical curriculum, designed to improve observable communication behaviors through actor-led simulation and multi-rater feedback. Methods: This single-group pre-post study evaluated a four-week mandatory intervention embedded within the clinical communication curriculum. Students were assessed before and after the intervention using a 19-item checklist covering five domains of empathy-related communication. Pre-post changes were analyzed using paired-sample t-tests and Cohen’s dz. Results: Thirty-two third-year medical students (mean age 21.25 years, 53% female) participated in the study. Post-intervention scores were significantly higher than baseline scores for the overall empathy-related communication score and across all five domains (all p ≤ 0.001). The overall mean score increased from 7.5 (SD 1.43) to 9.2 (SD 1.14), with a mean difference of 1.7 points (95% CI 1.12 to 2.28; p < 0.001), corresponding to a large paired-sample effect size (Cohen’s dz = 1.03). The largest domain-level gains were observed in verbal and non-verbal communication. Conclusions: This curriculum-embedded, actor-led empathy training intervention was associated with short-term improvement in observed empathy-related communication behaviors among third-year medical students. These findings support the feasibility of behaviorally grounded empathy training within undergraduate medical education, while highlighting the need for controlled and longitudinal studies to assess durability and transferability. Full article

Background: The increasing complexity of healthcare systems and the emphasis placed on patient safety have highlighted the crucial role of non-technical skills (NTS). Simulation-based training (SBT) has been widely adopted to acquire these skills. Objective: To evaluate the impact of simulation on the development of non-technical skills in health sciences students. Methods: This systematic review was conducted in accordance with the PRISMA 2020 guidelines and prospectively registered in PROSPERO (CRD420251066518). MEDLINE, Scopus, and Web of Science were searched from their inception to July 2025. Results: Twelve studies met the inclusion criteria. Most used quasi-experimental, before-and-after study designs and relied primarily on self-reported measures. The majority of results were classified at Kirkpatrick level 2a (attitude changes). Three studies reached level 2b, demonstrating improvements in objectively observed teamwork and communication behaviors. MERSQI scores ranged from 6.5 to 17, indicating variable methodological quality. No studies assessed transfer to clinical practice (level 3) or patient-level outcomes (level 4). Conclusions: Simulation-based training appears effective in improving self-reported non-technical skills and, to a lesser extent, objectively observed teamwork behaviors among health sciences students. However, the predominance of lower-level outcomes and methodological heterogeneity limit the strength of the evidence. Rigorous longitudinal studies evaluating higher-level outcomes are needed. Full article