Search Results (98)

Recent advances in large language models (LLMs) have made automated multiple-choice question (MCQ) generation increasingly feasible; however, reliably producing items that satisfy controlled cognitive demands remains a challenge. To address this gap, we introduce ReQUESTA, a hybrid, multi-agent framework for generating cognitively diverse MCQs that systematically target text-based, inferential, and main idea comprehension. ReQUESTA decomposes MCQ authoring into specialized subtasks and coordinates LLM-powered agents with rule-based components to support planning, controlled generation, iterative evaluation, and post-processing. We evaluated the framework in a large-scale reading comprehension study using academic expository texts, comparing ReQUESTA-generated MCQs with those produced by a single-pass GPT-5 zero-shot baseline. Psychometric analyses of learner responses assessed item difficulty and discrimination, while expert raters evaluated question quality across multiple dimensions, including topic relevance and distractor quality. Results showed that ReQUESTA-generated items were consistently more challenging, more discriminative, and more strongly aligned with overall reading comprehension performance. Expert evaluations further indicated stronger alignment with central concepts and superior distractor linguistic consistency and semantic plausibility, particularly for inferential questions. These findings demonstrate that hybrid, agentic orchestration can systematically improve the reliability and controllability of LLM-based generation, highlighting workflow design as a key lever for structured artifact generation beyond single-pass prompting. Full article

Generative AI (GenAI) challenges traditional technology integration frameworks by introducing agentic systems that actively participate in meaning-making, requiring educators to shift from tool operation to cognitive orchestration. This study introduces the MTA–TPACK Dynamic Collaboration Spiral, a theoretical framework that integrates Meta-Task Awareness (MTA) to explain how static knowledge resources are dynamically activated during human–AI collaboration. We empirically illustrate this framework through a two-phase scientific visualization task concerning typhoon–terrain interactions, utilizing Midjourney for human-led orchestration and GPT-4o for closed-loop refinement. The results demonstrate that successful integration requires translating abstract disciplinary knowledge into precise, AI-intelligible visual constraints rather than relying solely on technical prompting skills. Furthermore, we document how evaluation practices evolve from simple error correction to structured, AI-assisted diagnosis. The resulting visual artifacts embody Visible Pedagogical Thinking (VPT)—externalized cognitive constructs that make expert reasoning inspectable and reusable. By foregrounding evaluation-centered task design, this study provides a transferable, theoretically grounded account of how human–AI collaboration can remain pedagogically meaningful. The model contributes to sustainable pedagogical innovation by offering a roadmap for strengthening teachers’ long-term epistemic agency in AI-mediated design environments. Full article

This study examined whether large language models (LLMs) can generate clinically realistic profiles of cognitive decline and whether simulated deficits reflect architectural constraints rather than superficial role-playing artifacts. Using GPT-4o-mini, we generated synthetic cohorts (n = 10 per group) representing healthy aging, mild cognitive impairment (MCI), and Alzheimer’s disease (AD), assessed through a conversational neuropsychological battery covering episodic memory, verbal fluency, narrative production, orientation, naming, and comprehension. Experiment 1 tested whether synthetic subjects exhibited graded cognitive profiles consistent with clinical progression (Control > MCI > AD). Experiment 2 systematically manipulated prompt context in AD subjects (short, rich biographical, and few-shot prompts) to dissociate robust from manipulable deficits. Significant cognitive gradients emerged (p < 0.001) across eight of thirteen domains. AD subjects showed impaired episodic memory (Cohen’s d = 4.71), increased memory intrusions, and reduced narrative length (d = 3.07). Critically, structurally constrained memory tasks (episodic recall, digit span) were invariant to prompting (p > 0.05), whereas generative tasks (narrative length, verbal fluency) showed high sensitivity (F > 100, p < 0.001). Rich biographical prompts paradoxically increased memory intrusions by 343%, indicating semantic interference rather than cognitive rescue. These results demonstrate that LLMs can serve as in silico test benches for exploring candidate digital biomarkers and clinical training protocols, while highlighting architectural constraints that may inform computational hypotheses about memory and language processing. Full article

Introduction: Resting-state EEG (rsEEG) is a scalable window onto trait-like “executive readiness,” but findings have been fragmented by task impurity on the executive-function (EF) side and heterogeneous EEG pipelines. This review synthesizes rsEEG features that reliably track EF in healthy samples across development and aging and evaluates moderators such as cognitive reserve. Materials and methods: Following PRISMA 2020, we defined PECOS-based eligibility (human participants; eyes-closed/eyes-open rsEEG; spectral, aperiodic, connectivity, topology, microstate, and LRTC features; behavioral EF outcomes) and searched MEDLINE/PubMed, Embase, PsycINFO, Web of Science, Scopus, and IEEE Xplore from inception to 30 August 2025. Two reviewers were screened/double-extracted; the risk of bias in non-randomized studies was assessed using the ROBINS-I tool. Sixty-three studies met criteria (plus citation tracking), spanning from childhood to old age. Results: Across domains, tempo, noise, and wiring jointly explained EF differences. Faster individual/peak alpha frequency (IAF/PAF) related most consistently to manipulation-heavy working may and interference control/vigilance in aging; alpha power was less informative once periodic and aperiodic components were separated. Aperiodic 1/f parameters (slope/offset) indexed domain-general efficiency (processing speed, executive composites) with education-dependent sign flips in later life. Connectivity/topology outperformed local power: efficient, small-world-like alpha networks predicted faster, more consistent decisions and higher WM accuracy, whereas globally heightened alpha/gamma synchrony—and rigid high-beta organization—were behaviorally sluggish. Within-frontal beta/gamma coherence supported span maintenance/sequencing, but excessive fronto-posterior theta coherence selectively undermined WM manipulation/updating. A higher frontal theta/beta ratio forecasts riskier, less adaptive choices and poorer reversal learning for decision policy. Age and reserve consistently moderated effects (e.g., child frontal theta supportive for WM; older-adult slow power often detrimental; stronger EO ↔ EC connectivity modulation and faster alpha with higher reserve). Boundary conditions were common: low-load tasks and homogeneous young samples usually yielded nulls. Conclusions: RsEEG does not diagnose EF independently; single-band metrics or simple ratios lack specificity and can be confounded by age/reserve. Instead, a multi-feature signature—faster alpha pace, steeper 1/f slope with appropriate offset, efficient/flexible alpha-band topology with limited global over-synchrony (especially avoiding long-range theta lock), and supportive within-frontal fast-band coherence—best captures individual differences in executive speed, interference control, stability, and WM manipulation. For reproducible applications, recordings should include ≥5–6 min eyes-closed (plus eyes-open), ≥32 channels, vigilant artifact/drowsiness control, periodic–aperiodic decomposition, lag-insensitive connectivity, and graph metrics; analyses must separate speed from accuracy and distinguish WM maintenance vs. manipulation. Clinical translation should prioritize stratification and monitoring (not diagnosis), interpreted through the lenses of development, aging, and cognitive reserve. Full article

This article proposes a redefinition of scientific authorship under conditions of algorithmic mediation. We shift the discussion from the ontological dichotomy of “tool versus author” to an operationalizable epistemology of contribution. Building on the philosophical triad of instrumentality—intervention, representation, and hermeneutics—we argue that contemporary AI systems (notably large language models, LLMs) exceed the role of a merely “mute” accelerator of procedures. They now participate in the generation of explanatory structures, the reframing of research problems, and the semantic reconfiguration of the knowledge corpus. In response, we formulate the AI-AUTHorship framework, which remains compatible with an anthropocentric legal order while recognizing and measuring AI’s cognitive participation. We introduce TraceAuth, a protocol for tracing cognitive chains of reasoning, and AIEIS (AI epistemic impact score), a metric that stratifies contributions along the axes of procedural (P), semantic (S), and generative (G) participation. The threshold between “support” and “creation” is refined through a battery of operational tests (alteration of the problem space; causal/counterfactual load; independent reproducibility without AI; interpretability and traceability). We describe authorship as distributed epistemic authorship (DEA): a network of people, artifacts, algorithms, and institutions in which AI functions as a nonsubjective node whose contribution is nonetheless auditable. The framework closes the gap between the de facto involvement of AI and de jure norms by institutionalizing a regime of “recognized participation,” wherein transparency, interpretability, and reproducibility of cognitive trajectories become conditions for acknowledging contribution, whereas human responsibility remains nonnegotiable. Full article

In digital marketplaces, trust in e-commerce platforms has evolved from a protective heuristic into a powerful mechanism of behavioral conditioning. This review interrogates how trust cues such as star ratings, fulfillment badges, and platform reputation shape consumer cognition, systematically displace critical evaluation, and create asymmetries in perceived quality. Drawing on over 47 high-quality studies across experimental, survey, and modeling methodologies, we identify seven interlocking dynamics: (1) cognitive outsourcing via platform trust, (2) reputational arbitrage by low-quality sellers, (3) consumer loyalty despite disappointment, (4) heuristic conditioning through trust signals, (5) trust inflation through ratings saturation, (6) false security masking structural risks, and (7) the shift in consumer trust from brands to platforms. Anchored in dual process theory, this synthesis positions trust not merely as a transactional enabler but as a socio-technical artifact engineered by platforms to guide attention, reduce scrutiny, and manage decision-making at scale. Eventually, platform trust functions as both lubricant and leash: streamlining choice while subtly constraining agency, with profound implications for digital commerce, platform governance, and consumer autonomy. Full article

Mental fatigue significantly impairs student performance and learning outcomes, yet reliable neurophysiological assessment methods remain elusive in educational research. This systematic review examines electroencephalography (EEG) as an objective monitoring tool for mental fatigue in student populations, with particular focus on portable and wearable device applications. Following PRISMA guidelines, we systematically analysed 18 empirical studies (2012–2024, N = 595 participants, ages 10–32) employing continuous EEG during educational tasks. We evaluated frequency band definitions, EEG hardware configurations (from 4-channel portable devices to 64-channel research systems), electrode placements, preprocessing pipelines, and analytical approaches, including machine learning methods. Most studies identified increased frontal theta (4–8 Hz) and decreased beta (13–30 Hz) power as primary fatigue markers across diverse EEG systems. However, substantial methodological heterogeneity emerged: frequency band definitions varied considerably, preprocessing techniques differed, and small sample sizes (median N = 20) limited statistical power. While portable EEG systems demonstrate promise for objective, non-invasive cognitive state monitoring in naturalistic educational settings, current methodological inconsistencies constrain reliability and validity. This review identifies critical standardisation gaps and provides evidence-based recommendations for wearable EEG device development and implementation, including standardised protocols, automated artifact removal strategies, and validation linking EEG measures to educational outcomes. Full article

Dementia disrupts communication not only as a cognitive process but as a relational practice, leaving people living with dementia (PLwD) at risk of exclusion when language fragments. This study examines how communication closeness, the felt sense of being understood, emotionally attuned, and socially connected, might be supported through Research in and through Design (Ri&tD). Drawing on formative mixed-reality studies and a participatory co-design workshop with PLwD, caregivers, and stakeholders, we iteratively developed a series of playful artifacts culminating in Neighbourly, a tactile board game designed to support relational interaction through rule-based, multimodal play. Across this design genealogy, prototypes were treated as Living Counter-Maps: participatory mappings that made patterns of gesture, rhythm, shared attention, and material engagement visible and discussable. Through iterative interpretation and synthesis, the study identifies three guiding principles for designing for communication closeness: supporting co-regulation rather than correction, enabling multimodal reciprocity, and providing a shared material focus for joint agency. The paper consolidates these insights in the Living Counter-Maps Framework, which integrates counter-mapping and Ri&tD as a methodological approach for studying and designing relational communication in dementia care. Full article

Artifacts remain a major challenge in electroencephalogram (EEG) recordings, often degrading the accuracy of clinical diagnosis, brain computer interface (BCI) systems, and cognitive research. Although recent deep learning approaches have advanced EEG denoising, most still struggle to model long-range dependencies, maintain computational efficiency, and generalize to unseen artifact types. To address these challenges, this study proposes MDSC-VA, an efficient denoising framework that integrates multi-scale (M) depth-wise separable convolution (DSConv), variational autoencoder-based (VAE) latent encoding, and a multi-head self-attention mechanism. This unified architecture effectively balances denoising accuracy and model complexity while enhancing generalization to unseen artifact types. Comprehensive evaluations on three open-source EEG datasets, including EEGdenoiseNet, a Motion Artifact Contaminated Multichannel EEG dataset, and the PhysioNet EEG Motor Movement/Imagery dataset, demonstrate that MDSC-VA consistently outperforms state-of-the-art methods, achieving a higher signal-to-noise ratio (SNR), lower relative root mean square error (RRMSE), and stronger correlation coefficient (CC) values. Moreover, the model preserved over 99% of the dominant neural frequency band power, validating its ability to retain physiologically relevant rhythms. These results highlight the potential of MDSC-VA for reliable clinical EEG interpretation, real-time BCI systems, and advancement towards sustainable healthcare technologies in line with SDG-3 (Good Health and Well-Being). Full article

Cosmological scenarios wherein the cumulative number of spontaneously formed, cognitively impaired, disembodied transient observers is vastly larger than the corresponding number of atypical ‘ordinary observers’ (OOs) formed in the conventional way—essentially via cosmic evolution and gravitational instability—are disqualified in modern cosmology on the grounds of Cognitive Instability—the untrustworsiness of one own’s reasoning—let alone the atypicality of OOs like us. According to the concordance ΛCDM cosmological model—when described in the (expanding) ‘cosmic frame’—the cosmological expansion is future-eternal. In this frame we are atypical OOs, which are vastly outnumbered by typical Boltzmann Brains (BBs) that spontaneously form via sheer thermal fluctuations in the future-eternal asymptotic de Sitter spacetime. In the case that dark energy (DE) ultimately decays, the cumulative number of transient ‘Freak Observers’ (FOs) formed and destroyed spontaneously by virtue of the quantum uncertainty principle ultimately overwhelms that of OOs. Either possibility is unacceptable. We argue that these unsettling conclusions are artifacts of employing the (default) cosmic frame description in which space expands. When analyzed in the comoving frame, OOs overwhelmingly outnumber both BBs and FOs. This suggests that the dual comoving description is the cognitively stable preferred framework for describing our evolving Universe. In this frame, space is globally static, masses monotonically increase, and the space describing gravitationally bounded objects monotonically contracts. Full article

This study systematically investigates settlement sites that record living patterns of ancient humans, aiming to reveal the interactive mechanisms of human–environment relationships. The core issues of landscape archeology research are the surface spatial structure, human spatial cognition, and social practice activities. This article takes the Han Dynasty settlement site in Sanyangzhuang, Neihuang County, Anyang City, Henan Province, as a typical case. It comprehensively uses ArcGIS 10.8 spatial analysis and remote sensing image interpretation techniques to construct spatial distribution models of elevation, slope, and aspect in the study area, and analyzes the process of the Yellow River’s ancient course changes. A regional historical geographic information system was constructed by integrating multiple data sources, including archeological excavation reports, excavated artifacts, and historical documents. At the same time, the sequences of temperature and dry–wet index changes in the study area during the Qin and Han dynasties were quantitatively reconstructed, and a climate evolution map for this period was created based on ancient climate proxy indicators. Drawing on three dimensions of settlement morphology, architectural spatial organization, and agricultural technology systems, this paper provides a deep analysis of the site’s spatial cognitive logic and the ecological wisdom it embodies. The results show the following: (1) The Sanyangzhuang Han Dynasty settlement site reflects the efficient utilization strategy and environmental adaptation mechanism of ancient settlements for land resources, presenting typical scattered characteristics. Its formation mechanism is closely related to the evolution of social systems in the Western Han Dynasty. (2) In terms of site selection, settlements consider practicality and ceremony, which can not only meet basic living needs, but also divide internal functional zones based on the meaning implied by the orientation of the constellations. (3) The widespread use of iron farming tools has promoted the innovation of cultivation techniques, and the implementation of the substitution method has formed an ecological regulation system to cope with seasonal climate change while ensuring agricultural yield. The above results comprehensively reflect three types of ecological wisdom: “ecological adaptation wisdom of integrating homestead and farmland”, “spatial cognitive wisdom of analogy, heaven, law, and earth”, and “agricultural technology wisdom adapted to the times”. This study not only deepens our understanding of the cultural value of the Han Dynasty settlement site in Sanyangzhuang, but also provides a new theoretical perspective, an important paradigm reference, and a methodological reference for the study of ancient settlement ecological wisdom. Full article

Thise article examines the reliable online detection and IDS/IPS intrusion prevention in dynamic corporate networks problems, where traditional signature-based methods fail to keep pace with new and evolving attacks, and streaming data is susceptible to drift and targeted “poisoning” of the training dataset. As a solution, we propose a hybrid neural network system with adaptive online training, a formal minimax false-positive control framework, and a robustness mechanism set (a Huber model, pruned learning rate, DRO, a gradient-norm regularizer, and a prioritized replay). In practice, the system combines modal encoders for traffic, logs, and metrics; a temporal GNN for entity correlation; a variational module for uncertainty assessment; a differentiable symbolic unit for logical rules; an RL agent for incident prioritization; and an NLG module for explanations and the preparation of forensically relevant artifacts. In this case, the applied components are connected via a cognitive layer (cross-modal fusion memory), a Bayesian-neural network fuser, and a single multi-task loss function. The practical implementation includes the pipeline “novelty detection → active labelling → incremental supervised update” and chain-of-custody mechanisms for evidential fitness. A significant improvement in quality has been experimentally demonstrated, since the developed system achieves an ROC AUC of 0.96, an F1-score of 0.95, and a significantly lower FPR compared to basic architectures (MLP, CNN, and LSTM). In applied validation tasks, detection rates of ≈92–94% and resistance to distribution drift are noted. Full article

The mainstream launch of generative AI video platforms represents a major change to the socio-technical system of digital media, raising critical questions about public perception and societal impact. While research has explored isolated technical or ethical facets, a holistic understanding of the user experience of AI-generated videos—as an interrelated set of perceptions, emotions, and behaviors—remains underdeveloped. This study addresses this gap by conceptualizing public discourse as a complex system of interconnected themes. We apply a mixed-methods approach that combines quantitative LDA topic modeling with qualitative interpretation to analyze 11,418 YouTube comments reacting to AI-generated videos. The study’s primary contribution is the development of a novel, three-tiered framework that models user experience. This framework organizes 15 empirically derived topics into three interdependent layers: (1) Socio-Technical Systems and Platforms (the enabling infrastructure), (2) AI-Generated Content and Esthetics (the direct user-artifact interaction), and (3) Societal and Ethical Implications (the emergent macro-level consequences). Interpreting this systemic structure through the lens of the ABC model of attitudes, our analysis reveals the distinct Affective (e.g., the “uncanny valley”), Behavioral (e.g., memetic participation), and Cognitive (e.g., epistemic anxiety) dimensions that constitute the major elements of user experience. This empirically grounded model provides a holistic map of public discourse, offering actionable insights for managing the complex interplay between technological innovation and societal adaptation within this evolving digital system. Full article

Cultural heritage museums, as integral components of the urban built environment and public cultural space, not only preserve historical memory but also subtly shape visitors’ psychological experiences and well-being. Yet the mechanisms linking museum environmental quality with visitor mental experiences remain insufficiently explored. Drawing on 10,684 visitor reviews collected from Dianping, Weibo, and Ctrip, this study applies text mining and semantic analysis to construct an evaluation framework of visitor behavioral preferences and psychological experiences in heritage museums. The findings show that attention to spatial remains, historical artifacts, and cultural symbols is closely associated with positive emotions such as mystery, awe, and beauty, while adverse environmental conditions such as queuing and crowding often trigger negative feelings including fatigue, disappointment, and boredom. Further analysis reveals a clear pathway linking objects, behaviors, and experiences: spatial remains evoke psychological resonance through immersive perceptions of authenticity; artifacts are primarily linked to visual pleasure and emotional comfort; and cultural symbols are transformed into cognitive gains and spiritual meaning through interpretation and learning. Cross-regional comparison highlights significant differences among museums with distinct cultural backgrounds in terms of architectural aesthetics, educational value, and emotional resonance. This study not only offers a practical framework for the refined management and spatial optimization of heritage museums, but also demonstrates that high-quality cultural environments can promote mental health and emotional restoration. The results extend the interdisciplinary framework of museum research and provide empirical evidence for environmental improvement and public health promotion in cultural heritage spaces in the digital era. Full article

The conceptual design phase in architecture establishes the foundation for subsequent design decisions and influences up to 80% of a building’s lifecycle environmental impact. While Building Information Modeling (BIM) demonstrates transformative potential for sustainable design, its application during conceptual design remains constrained by perceived technical complexity and limited support for abstract thinking. This research examines how BIM tools can facilitate conceptual design through diagrammatic reasoning, thereby bridging technical capabilities with creative exploration. A mixed-methods approach was employed to develop and validate a Diagrammatic BIM (D-BIM) framework. It integrates diagrammatic reasoning, parametric modeling, and performance evaluation within BIM environments. The framework defines three core relationships—dissection, articulation, and actualization—which enable transitions from abstract concepts to detailed architectural forms in Revit’s modeling environments. Using Richard Meier’s architectural language as a structured test case, a 14-week quasi-experimental study with 19 third-year architecture students assessed the framework’s effectiveness through pre- and post-surveys, observations, and artifact analysis. Statistical analysis revealed significant improvements (p < 0.05) with moderate to large effect sizes across all measures, including systematic design thinking, diagram utilization, and academic self-efficacy. Students demonstrated enhanced design iteration, abstraction-to-realization transitions, and performance-informed decision-making through quantitative and qualitative assessments during early design stages. However, the study’s limitations include a small, single-institution sample, the absence of a control group, a focus on a single architectural language, and the exploratory integration of environmental analysis tools. Findings indicate that the framework repositions BIM as a cognitive design environment that supports creative ideation while integrating structured design logic and performance analysis. The study advances Education for Sustainable Development (ESD) by embedding critical, systems-based, and problem-solving competencies, demonstrating BIM’s role in sustainability-focused early design. This research provides preliminary evidence that conceptual design and BIM are compatible when supported with diagrammatic reasoning, offering a foundation for integrating competency-based digital pedagogy that bridges creative and technical dimensions of architectural design. Full article