Search Results (921)

Population ageing, social isolation, and limited opportunities for meaningful contact between generations pose important challenges to healthy ageing and community well-being. This mixed-methods pilot study examined the feasibility, acceptability, co-creation process, and preliminary outcomes of the P-IN Programme, a community-based intergenerational intervention involving preschool children and older adults living in residential care. The programme was developed and implemented in partnership with a preschool and a residential care facility in Portugal and comprised ten intergenerational sessions shaped through an iterative co-creation process involving participants, professionals, and researchers. Twenty-three children, eighteen older adults, and five professionals contributed to the study. Quantitative pre–post assessment explored fluency in both generations and cognitive functioning, quality of life, subjective well-being, and social participation in older adults. Qualitative data examined intergenerational beliefs, participants’ experiences, relationships, and reciprocal learning, while also informing the co-creation process. Most quantitative outcomes remained stable, although cognitive functioning showed a moderate, non-significant effect. Perceived quality of life changed significantly in an unfavourable direction. In contrast, qualitative findings revealed broader and more relational representations of the other generation, alongside shared experiences and clearer recognition of reciprocal learning. The integration of both components showed that positive relational and experiential changes were not necessarily accompanied by changes in broader standardised outcomes. Overall, the P-IN Programme was feasible to implement and generally well received within the two participating community settings. These findings support further evaluation of co-created intergenerational programmes using larger samples, controlled designs, and longer-term follow-up. Full article

Background: Memory influences decision-making regarding eating behaviour. However, marketing utilises our memory to exploit our consumptive habits. Whether we have an inherent bias towards memorising visual cues associated with high-calorie foods and how such bias contributes to our health outcomes remains to be determined. We therefore aimed to develop and validate a computerized, image-based Food Memory Bias Task (FMBT) for use in multiethnic Asian populations. Methods: In the development phase involving 172 participants, 91.9% rated the instrument as user-friendly and we optimised the instrument format into 12 visual food cues and 30-min delay period to avoid a ceiling effect. In the validation phase involving 184 multi-ethnic Asian participants (49.3 (14.5) years old, 44.6% male, 71.7% Chinese), FMBT memory score was associated with the brief assessment of cognition memory (β(p) = 0.23 (5.1 × 10⁻⁶)), and general cognition score ‘g’ (β(p) = 0.30 (6.5 × 10⁻⁹)). Results: Higher memory bias was associated with lower memory score in the low-calorie bias group ($r=-0.33$, $p=0.002$), but not in the hig h-calorie bias group. In this present study, despite randomly administering two versions of the test across visits, the FMBT bias score demonstrates a significant learning effect and low reproducibility (r(p) = −0.14 (0.11) across visits). Food memory bias score is associated with higher food approach traits according to the Adult-Eating Behaviour Questionnaire (β(p) = 0.23(0.015)), independent of age, sex, and ethnicity. Conclusion: FMBT provides an opportunity to address epidemiological questions on how food memory bias influences dietary habit and cardiometabolic health, and how it is in turn shaped by our built environment. Full article

Artificial intelligence is becoming deeply embedded in higher education, yet how the characteristics of AI-based e-learning systems relate to students’ perceived learning effectiveness and satisfaction remains insufficiently understood. This study examines the relationships of AI Functionality Compatibility, AI Instructional Process Coverage, and AI-Assisted Learning Cognitive Usability with Perceived Online Learning Effectiveness and Online Learning Satisfaction. Data from 384 students at Chinese universities were analyzed using a two-stage approach combining partial least squares structural equation modeling and artificial neural networks (PLS-SEM-ANN). The results showed that all three system characteristics were positively associated with perceived learning effectiveness, with instructional process coverage showing the strongest relationship. Cognitive usability also had a significant direct association with learning satisfaction, whereas functionality compatibility and instructional process coverage showed significant indirect effects through perceived learning effectiveness. The findings reveal an outcome-oriented pattern in which perceived learning effectiveness occupies a central position between system characteristics and satisfaction. This study extends understanding of AI-supported learning systems by emphasizing the alignment of technical functions with pedagogical processes and learners’ cognitive needs. It also provides practical guidance for universities and developers seeking to better align the design and evaluation of AI-based e-learning systems with learners’ instructional and cognitive needs. Full article

Generative artificial intelligence increasingly produces text, images, feedback, summaries, advertisements, synthetic faces, and audiovisual content evaluated alongside human-produced material. This systematic review synthesized comparative eye-tracking evidence on visual attention, cognitive processing, and engagement with AI-generated content. Searches of Scopus, Web of Science, and PubMed yielded 896 records; 23 studies met the eligibility criteria and contributed 778 participants in the review-relevant eye-tracking components. The evidence covered textual, static visual, audiovisual, and interactive outputs. Across heterogeneous designs and tasks, no modality-independent gaze pattern emerged. AI-generated material sometimes attracted more focal inspection, sometimes received less task-relevant attention, and often redistributed gaze across interface elements. Where supported by task characteristics or complementary outcomes, longer viewing was more often associated with processing difficulty, uncertainty, or checking than with preference. Generated summaries supported learning in some settings, whereas realistic synthetic media remained difficult to identify despite focused inspection. Methodological appraisal identified recurrent limitations in sampling, stimulus matching, confounder control, eye-tracking reporting, and documentation of model versions, prompts, generation settings, and output selection. Observed gaze differences were context-dependent and varied with modality, task, comparator, source belief, expertise, output quality, and measurement choices. Standardized reporting and stronger links between gaze and functional outcomes are needed for cumulative inference. Full article

Numbers-skills are affected in as many as 65% of cases following a stroke or a brain-injury, a condition termed ‘acalculia’. Post-stroke numerical skills interventions have to-date focused on using intense repetitions (‘drill’), in one-to-one settings. However, while ‘drill’ is needed, there is increasing evidence that learning (and re-learning) of numerical skills may be enhanced by additional techniques, informed by current educational and cognitive neuroscience theories. We report on a mixed-methods feasibility study examining the acceptability and impact of the first reported group intervention for acalculia, that is based on the principles of embodied learning, playfulness, and enriched social environment. Four brain-injury survivors took part in six-weekly 45-min group sessions involving games with numbers, accompanied with congruent movements. Following a 4-week break, n = 3 took part in additional three sessions. Performance on number skills (theoretical and functional) was collected before the intervention (T0), after six weeks (T1) and after further three weeks (T2) using validated batteries. Qualitative data were collected 3-months post intervention using semi-structured interviews with two patients. Results show improvements on all measures at both T1 and T2, including far-transfer to untrained problems. Qualitative findings emphasised the importance of group-settings, and the impact of playful learning on cognition, engagement, learning, confidence and wellbeing. We conclude that playful group therapy integrating modern educational theories is feasible and can be effective for improving numerical skills and speculate on the possible neural mechanisms that may facilitate these findings. Future work should evaluate the impact of combining multi-sensory, movement and cognitive rehabilitation in improving patients’ linguistic, physiological, and wellbeing outcomes. Full article

Serious games have demonstrated significant potential for supporting learning, cognitive stimulation, and skill development. However, most existing solutions rely predominantly on visual and auditory interaction, while the integration of real tactile experiences remains limited despite their potential to support richer multisensory interaction. Frutopia is a hybrid tangible serious game designed to integrate physical interaction with digital gameplay in order to support tangible interaction and tactile exploration. The game was developed following a structured process that combines the iPlus methodology for educational game design with the Scrum agile framework, enabling the systematic definition, implementation, and refinement of gameplay mechanics, tangible interaction, and usability-oriented features. The resulting system incorporates tangible user interaction through conductive physical objects with different textures connected via a Makey Makey interface, enabling players to control in-game actions through real tactile exploration. The game features progressive maze-based challenges inspired by Ecuadorian cultural environments and representative fruits from Ecuadorian regions, integrating multisensory feedback, gamification techniques, and embodied interaction principles to foster engagement and sensory exploration. The system was implemented using the Godot Engine and evaluated through functionality and usability assessments. Functional validation achieved a success rate of 94.74% across the defined test cases, demonstrating the technical stability of the proposed solution. Additionally, a usability evaluation involving 50 participants was conducted using the Serious Games Usability Evaluation Instrument (SGUEI). The assessment produced a final rating of 90.37%, reflecting favorable perceptions of the interaction quality and overall user experience. The results demonstrate the feasibility of integrating tangible interaction and multisensory feedback within serious game environments and suggest that hybrid tangible interfaces can enrich user engagement and interaction quality. This work contributes to the design and development of hybrid tangible serious games by presenting a structured development workflow and providing preliminary evidence of the technical feasibility and usability of tangible interaction in serious game environments. The proposed system establishes a foundation for future studies involving the intended target population and the evaluation of educational and cognitive outcomes. Full article

This paper aims to evaluate the usability and user experience of ad hoc augmented reality systems in the learning experience of mechanical engineering students, focusing on fluid machinery education. Three mobile augmented reality applications were developed to support teachers in explaining the main parts of an impeller blade and its fluid interaction, integrating computer-aided design (CAD) models with velocity and pressure maps derived from computational fluid dynamics (CFD) simulations. By providing multiple means of representation, these tools were developed to support the explanation of complex 2D concepts without requiring specialized hardware. The obtained results revealed that the usability of the developed applications, assessed through the System Usability Scale (SUS), was remarkably effective. Furthermore, the User Experience Questionnaire (UEQ) showed that the average scores for each evaluation criterion were highly positive, especially in the “Stimulation” and “Novelty” areas. Accurate statistical analyses revealed that students’ feedback was not influenced by users’ familiarity with virtual and augmented reality tools. In conclusion, since no objective learning gains were evaluated, this investigation’s outcomes indicate that the developed applications provide an engaging tool with high usability and positive user experience, framing inclusive education as a fundamental design rationale rather than an empirically demonstrated outcome and laying the groundwork for future studies to objectively measure cognitive impact. Full article

The rapid integration of generative artificial intelligence (GenAI) into educational settings has raised growing concerns regarding its potential influence on students’ higher-order thinking skills. However, existing findings remain inconclusive, and the role of individual factors in shaping these effects has received limited attention. Drawing on self-regulated learning theory, this study examined the relationships between GenAI usage and three higher-order thinking skills, critical thinking, creative thinking, and computational thinking, and investigated the moderating role of self-regulated learning. The results indicated that the associations between GenAI use and higher-order thinking skills varied across levels of self-regulated learning. Specifically, among students with lower levels of self-regulated learning, GenAI use was negatively associated with critical thinking, creative thinking, and computational thinking. In contrast, among students with higher levels of self-regulated learning, GenAI use was positively associated with critical thinking and computational thinking, while its association with creative thinking was not significant. Furthermore, self-regulated learning was positively associated with all three higher-order thinking skills and significantly moderated the relationships between GenAI use and critical thinking, creative thinking, and computational thinking. These findings highlight the complex cognitive consequences of GenAI usage and underscore the importance of self-regulated learning in shaping students’ cognitive outcomes in AI-assisted learning environments. The study contributes to a more nuanced understanding of how GenAI influences higher-order thinking and provides implications for promoting effective and responsible AI use in education. Full article

Learning potential assessment is important for understanding individual differences in learning processes and cognitive development. Traditional assessments mainly rely on questionnaires or behavioral data collected during tasks, emphasizing external performance while lacking sensitivity to learners’ internal cognitive activities. Therefore, they provide limited evidence about the neurocognitive mechanisms underlying individual learning potential. This study investigated whether task-evoked EEG responses acquired during a dynamic learning paradigm could characterize individual differences in task-specific learning performance under graduated instructional support. Participants were 50 undergraduate students who completed a dynamic cognitive task designed to evaluate learning performance under graduated instructional support. An electroencephalogram (EEG)-based Multi-scale Learning Potential Assessment (MS-LPA) framework was developed, including a Dynamic Learning Paradigm with progressively complex cognitive tasks and responsive cueing; multiscale Neural Complexity Representations quantifying signal complexity and inter-regional synchronization; and a ResNet-18 model with SHAP-based interpretability for classification and biomarker identification. Experiments showed that MS-LPA captured task-evoked neural responses and outperformed traditional outcome-driven evaluation schemes. Entropy-based features achieved an accuracy of 0.77, and functional connectivity matrices reached 0.83. These findings demonstrate the feasibility of using EEG-derived neural representations to differentiate individual differences in task-specific dynamic learning performance and provide insights into its underlying neural mechanisms. Full article

Persistent challenges in Environmental Microbiology teaching include the mismatch between networked knowledge structures and linear instruction, insufficient support for open-ended engineering scenarios, the gap between blended formats and deep learning outcomes, and weak process-oriented assessment evidence. Guided by Outcome-Based Education (OBE), the course team constructed a four-dimensional curriculum graph integrating ability, problem, knowledge, and resource dimensions, and developed a progressive teaching model of graph-guided learning, intelligent inquiry, and data-driven evaluation. This article reports the design and implementation of a teacher–student–AI collaborative blended smart course and presents a descriptive evaluation of one implementation cycle. The course was implemented over a 16-week semester with 60 third-year environmental engineering undergraduates at Jiangsu University; the previous cohort, taught by the same instructors with conventional blended teaching, served as a historical reference. Data were collected from platform interaction records, blueprint-matched final examination questions, and a piloted questionnaire (58 valid responses; Cronbach’s α = 0.89), and were analyzed with descriptive statistics, independent samples t-tests, and effect sizes; classroom interaction was summarized descriptively because repeated student session observations were clustered within students. Using the module on biological nitrogen and phosphorus removal in wastewater treatment as an example, the article illustrates differentiated pre-class preparation, in-class problem-chain inquiry with AI-assisted scheme design, and post-class learning profiling. Compared with the historical cohort, the implementation cohort showed a descriptively higher classroom interaction rate (90.3% vs. 70.5%), a 15.2-percentage-point higher mean score rate on examination questions targeting microbial regulation (95% CI [10.2, 20.2]; Cohen’s d = 1.12), and positive student perceptions (91.4% reported that knowledge graphs supported systematic cognition and 84.5% reported increased confidence in analyzing engineering problems). The documented course architecture and implementation pathway may provide a practical reference for the smart transformation of core environmental engineering courses. Full article

Background: Iodine is an essential micronutrient required for foetal development, cognitive function, and metabolic regulation; however, suboptimal iodine status remains a public health concern in Europe and other regions. Improving food literacy related to iodine may support healthier dietary choices during adolescence, a critical life stage for establishing long-term habits. This intervention development study describes the development of The ABC of Iodine Teaching Programme within the EUthyroid2 project, designed to enhance iodine-related knowledge and awareness among adolescents aged 13–17 years across six regions (UK, Republic of Cyprus, Slovenia, Germany, Bangladesh, and Pakistan). Methods: The intervention was developed according to the Behaviour Change Wheel, targeting capability, opportunity, and motivation, and informed by guidance for complex interventions. The programme was comprised of three flexible, culturally adapted modules integrating lectures on iodine physiology, deficiency risks, WHO recommendations, and locally relevant dietary sources. Active learning strategies, including collaborative tasks and personalised feedback through an Iodine Feedback Tool, were included. Materials were translated and adapted to local contexts and implemented in a hybrid format combining printed booklets with QR-linked digital resources. Results: The primary outcome of the intervention development process was The ABC of Iodine Teaching Programme, a multi-component, scalable educational intervention aligned with principles of food literacy, active learning and behaviour change theory. It incorporated behaviour change techniques and context-specific adaptations to facilitate engagement and knowledge acquisition in school settings. The programme is currently under evaluation in participating regions. Conclusions: This study presents the systematic development of a complex teaching programme. By combining behaviour change theory with innovative and context-sensitive educational strategies, The ABC of Iodine Teaching Programme provides a flexible and scalable framework for improving iodine-related food literacy among adolescents. Its effectiveness will be determined through the ongoing evaluation studies. Full article

Artificial intelligence (AI) is rapidly being integrated into university curricula, yet quantitative indicators of AI use reveal little about how learners use AI as a learning resource or what educational outcomes follow. This cross-sectional survey study of 212 university students enrolled in AI-integrated courses examined the associations of AI literacy, instructor feedback, and two single-item AI use indicators—the proportion of in-class AI use and total weekly AI use time—with self-perceived cognitive learning outcomes, measured across the six cognitive processes of the revised Bloom’s taxonomy. Confirmatory factor analyses supported multidimensional and higher-order structures, but the cognitive domains overlapped substantially (interfactor correlations up to 0.943; HTMT up to 0.946), so domain-level distinctions should be interpreted with caution. A regression model with the four predictors explained 46.3% of the variance in overall self-perceived cognitive learning outcomes (R² = 0.463, adjusted R² = 0.452). When all predictors were considered simultaneously, only AI literacy showed a significant positive association (B = 0.615, β = 0.593, 95% CI [0.475, 0.754], p < 0.001); the data did not provide evidence for independent associations of instructor feedback or the two AI use indicators, whose weaker associations may partly reflect their single-item measurement. AI literacy remained significantly associated with all six cognitive domains after Benjamini–Hochberg correction. These findings suggest—within the limits of a cross-sectional, self-report design—that the quantity of AI use and learners’ competency to understand, evaluate, and self-regulate AI use are empirically distinct indicators that universities should measure separately. Full article

Cities increasingly use resilience policies to manage climate risk, but it remains unclear how these policies alter firms’ green strategies. Using a difference-in-differences design, we analyze 2011–2023 panel data for A-share listed firms around China’s Climate-Resilient City Construction (CRCC) pilot. CRCC exposure is associated with a higher text-based measure of corporate green transformation. The estimate is stable in parallel-trends and placebo tests, alternative outcome measures, exclusions of confounding policies and shocks, alternative specifications, double machine learning, pre-policy propensity-score matching, and instrumental-variable estimation. Mechanism estimates are consistent with three channels through which CRCC may affect firm behavior. CRCC strengthens executive green cognition, redirects environmental spending toward prevention, and supports green technological and management innovation. By contrast, the estimate for end-of-pipe investment is small and statistically insignificant. A pooled interaction test finds differences across firm life-cycle stages. Comparisons by city size, exemplary-city status, and supply-chain resilience remain descriptive because their grouping indicators are unavailable for pooled re-estimation. Spatial estimates reveal limited, non-monotonic spillovers. Overall, the results indicate that an urban adaptation policy can influence firm strategy, subject to the text-based outcome and the sample of Chinese listed firms. Full article

Whether agricultural cultural heritage (ACH) tourism supports sustainable heritage development depends partly on whether visits sustain the communication and internalization of heritage knowledge rather than merely produce favorable consumption evaluations. Drawing on experience economy theory, social identity theory, and tourism learning theory, this study constructs a mechanism model linking ACH experiences to tourists’ cultural learning. To reduce conceptual overlap, experience quality is defined as visitors’ perceived distinctiveness, novelty, and memorability of the visit, whereas perceived value is defined as a utilitarian assessment of gains relative to the money, time, and effort invested. ACH experience is therefore divided into three analytically distinct dimensions: experience quality, perceived value, and knowledge transfer. Cultural identity is introduced as a mediating variable and is understood as a layered psychological construct including cognitive recognition, affective attachment, and evaluative commitment. Income level is introduced as a moderating variable and is interpreted as a within-sample socioeconomic boundary condition after controlling for education and other demographic characteristics. In this study, sustainability is defined narrowly as the sociocultural continuity of ACH from the visitor side and is operationalized through perceived knowledge transfer, cultural identity, and self-reported cultural learning; income is used to examine whether this pathway differs across socioeconomic groups. Based on 430 valid questionnaires collected from visitors to ACH sites in Fujian Province, this cross-sectional study conducts empirical tests using structural equation modeling (SEM), Bootstrap mediation analysis, and hierarchical regression analysis. The results show that experience quality and knowledge transfer are positively associated with tourists’ cultural learning, with knowledge transfer showing the stronger association. Perceived value has no significant relationship with cultural learning, indicating that a consumption-level perception of value for money does not necessarily correspond to deeper cultural learning. Cultural identity fully mediates the statistical relationship between knowledge transfer and tourists’ cultural learning, revealing a dominant pathway linking knowledge transfer, cultural identity, and cultural learning. Income level positively moderates the relationship between cultural identity and tourists’ cultural learning. Because the data are cross-sectional and self-reported, these findings indicate theoretically consistent associations rather than definitive causal effects. The study contributes to sustainability research by offering a quantifiable visitor-side framework for monitoring knowledge communication, cultural connection, and learning-related outcomes. Repeated measurement of these indicators may support adaptive heritage management and inclusive cultural participation, but they should be combined with ecological, economic, and community-based indicators rather than treated as a complete sustainability index. Full article

Background/Objectives: Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are frequently co-occurring neurodevelopmental conditions with partially overlapping neurophysiological profiles. Electroencephalography (EEG) provides non-invasive access to candidate biomarkers, yet the literature remains largely organized around single-diagnosis frameworks, limiting comparison across conditions and constraining translation into intervention selection. This review compares EEG signatures across ASD and ADHD from a transdiagnostic perspective and examines how such signatures might inform the selection of non-pharmacological interventions. Methods: A structured search of PubMed, Scopus, IEEE Xplore and Web of Science identified peer-reviewed studies published between 2010 and 2026 reporting EEG findings in ASD and/or ADHD, spanning resting-state, task-based, connectivity, event-related potential, machine learning and intervention studies. Sixty-eight sources were synthesized thematically. Given substantial heterogeneity in acquisition parameters and analytic pipelines, evidence was integrated interpretively rather than pooled quantitatively, and no formal risk-of-bias assessment was undertaken. Results: Shared features across both conditions frequently included low-frequency theta excess, reduced alpha modulation under cognitive load, and flattened aperiodic (1/f) slopes—a pattern compatible with, though not a direct measurement of, altered excitation/inhibition balance. While substantial heterogeneity exists, disorder-specific signatures often comprised the ASD “U-shaped” spectral profile alongside elevated epileptiform activity, and frontally pronounced theta/beta ratio elevation in subsets of individuals with ADHD. Machine-learning studies increasingly emphasize interpretable, multidomain feature sets over binary classification. Mindfulness-based and neurofeedback interventions converge on theta reduction and alpha enhancement, although reported effects are frequently conditional on responder status, task context, or outcome-rater blinding. Conclusions: Convergent EEG features support a transdiagnostic account of neurodevelopmental dysregulation. A biomarker-informed framework for intervention selection is proposed, which requires prospective validation before clinical application. Full article