Search Results (143)

Artificial intelligence (AI) is increasingly embedded in marketing work, yet employees who use AI tools may not always disclose AI’s role in producing their outputs. This study examines AI disclosure silence, defined as employees’ intentional withholding of information about the use, role, or contribution of AI tools in work-related outputs after AI has already been used. Unlike AI avoidance or resistance, this construct concerns post-adoption concealment; unlike general employee silence, it focuses on the hidden technological contribution behind visible work. Drawing on Conservation of Resources Theory and Psychological Safety Theory, the study investigates how threat-based conditions, safety and governance conditions, and AI-related capability are associated with AI disclosure silence. Data were collected through a two-wave survey of 635 marketing employees who actively used AI tools at work. The analysis combined measurement validation, Necessary Condition Analysis (NCA), fuzzy-set Qualitative Comparative Analysis (fsQCA), and explainable machine learning. The findings show that no single condition operated as a strong necessary bottleneck. Instead, AI disclosure silence appeared through multiple pathways involving AI anxiety, fear of negative evaluation, perceived creativity threat, perceived job insecurity, low trust in management, weak psychological safety, and unclear AI policy. SHapley Additive exPlanations (SHAP)-based interpretation further indicated that fear of negative evaluation, AI anxiety, perceived creativity threat, and trust in management had the strongest model-based predictive relevance. The study contributes to workplace AI and employee silence research by positioning AI disclosure silence as an emerging post-adoption disclosure construct. It also highlights the need for clear AI disclosure norms, non-punitive managerial responses, AI-assisted authorship guidelines, and psychologically safe AI-governance practices. The findings should be interpreted as configurational and predictive evidence rather than causal effects, and further scale validation across sectors and cultures is encouraged. Full article

Artificial intelligence, particularly after the recent explosive advances and widened uses, has fired up the (previously quiet) debates about the nature of reasoning, creativity, and agency. This paper examines these issues through the lens of classical kalām (Islamic theology), with some focus on Muʿtazilite principles. It begins by presenting a concise overview of the major schools of kalām (Muʿtazilism, Ashʿarism, and Māturīdism), highlighting their respective treatments of reason (ʿaql), divine creation, and human action. Then a brief review of modes of reasoning is provided, shedding light on differences between human and artificial reasoning, stressing the distinction between statistically generated outputs and contextually grounded, meaning-oriented cognition. Then, drawing on Muʿtazilite conceptions of reason, objective morality, and true human agency, in particular, the paper argues that contemporary AI systems, despite their impressive capabilities, do not satisfy the conditions for knowledge (ʿilm), creation (khalq), or agency (fiʿl) in the theological sense. It is argued that although they may appear “creative” or displaying origination (ihdāth) capabilities, AI systems, so far and to the extent that current developments seem to indicate, lack the essential features of ʿaql (reason), nafs (soul), rūḥ (spirit), and niyyah (intention) that Islamic theology identifies as the true, defining aspects of human beings. Full article

Large language models (LLMs) are increasingly being used as assistants for creative design tasks. In knowledge-intensive, highly constrained design tasks, LLM prompt configuration may affect model responses across user needs, accessibility principles, technical feasibility, and implementation conditions, thereby affecting design output quality. To examine this relationship, this paper investigates the effects of a standard LLM assistant, divergent persona prompting, and a Domain-Structured Persona Prompting (DSPP) framework on design outcomes for age-friendly health monitoring wearable devices. Fifty-three participants with design backgrounds completed design tasks under these three prompt configurations. The resulting proposals were then evaluated by experts, and further analyzed through user experience questionnaires, domain knowledge invocation coding, and LLM-as-a-Judge-assisted validation. Results indicate that DSPP enhances the overall quality of design proposals, with particular advantages in age-friendliness, practicality, and implementation feasibility; DSPP-generated proposals also more frequently incorporate standards-based rationale, older user characteristics, and age-friendly design principles. This research shows the potential of DSPP in LLM-assisted professional tasks with complex requirements and constraints. Full article

This study examines the impact of three creative thinking methods—mind mapping, brainstorming and SCAMPER—on the generation of fashion sketches using artificial intelligence-generated content (AIGC). While AIGC is becoming more prevalent in design practice, little research has examined how various ideation strategies influence the quality and originality of AI-generated results. For this study, ten professional designers created prompts using each method and generated fashion sketches via an AIGC platform. A total of 204 valid responses to the evaluation were collected, assessing the outputs across five dimensions: design support capability, quality, originality, detail refinement, and market acceptability. The results show that brainstorming most effectively enhances design quality and visual detail, while mind mapping yields the highest market acceptability. SCAMPER stimulates originality but performs less favorably in terms of refinement and visual coherence. These findings demonstrate that creative thinking methods significantly influence AIGC-assisted design and highlight the importance of aligning ideation strategies with the cognitive preferences and task objectives of designers. Despite limitations relating to the relatively small sample size of participating designers and reliance on a single AIGC platform, this study is the first to provide a systematic comparison of these three creative thinking methods within the context of AIGC-assisted fashion design. The findings offer new insights into the integration of structured creative thinking with AIGC tools in both design education and professional practice, while future research is encouraged to expand the diversity of participants, design tasks, and generative systems. Full article

The increasing use of large language models (LLMs) in enterprises creates a need for routing mechanisms that select models according to both technical performance and organizational preferences. This article proposes a multicriteria decision-support framework for enterprise LLM routing that combines AHP-based criterion weighting with SAW-based prompt-level model selection. The framework evaluates prompts according to criteria related to required accuracy, business risk, reasoning depth, cost sensitivity, response-time sensitivity, standardization, and creativity. The empirical evaluation was conducted on 500 heterogeneous business prompts, using GPT-5-nano as the prompt-scoring router, GPT-4o-mini as the cheaper response model, and GPT-5 as the stronger response model. Costs were calculated from actual input and output token counts, including routing overhead. Response sufficiency was assessed using a structured LLM-as-a-judge protocol with three evaluator profiles. The proposed SAW routing variant with confidence margin and risk veto achieved a sufficiency rate of 94.4%, compared with 94.6% for the always-strong strategy and 86.8% for the always-cheap strategy. Relative to always-strong routing, it reduced total cost by 37.4%, with only a 0.2 percentage-point decrease in sufficiency. The framework was also compared with keyword-risk, token-threshold, TF-IDF centroid, logistic-regression, multiplicative-SAW, and TOPSIS baselines. The results indicate that an interpretable multicriteria router can achieve near-strong-model response sufficiency at substantially lower cost while preserving auditability and alignment with enterprise decision criteria. Full article

Sustainable regional development requires understanding how entrepreneurship and unemployment co-evolve. This study investigates this relationship across Türkiye’s 26 Nomenclature of Territorial Units for Statistics 2 regions over the 2007–2024 period, testing the Schumpeter (pull) and Refugee (push) effects with controls for regional economic and financial determinants. Using the Dynamic Common Correlated Effects estimator, which accounts for cross-sectional dependence and slope heterogeneity across regions, the analysis provides evidence supporting both effects, while revealing that neither effect emerges instantaneously. The Schumpeter effect operates with an approximately one-year lag, reflecting the time new ventures require to complete organizational formation and generate net labor demand, with a creative destruction dynamic appearing from the second year onward. The Refugee effect materializes within one to two years, as unemployed individuals exhaust formal job search alternatives before turning to necessity entrepreneurship. Critically, the findings identify banking sector intermediation efficiency, rather than aggregate credit volume, as a more consistent financial channel for sustainable labor market outcomes, and document a pattern consistent with jobless growth, in which regional output expansion has not systematically translated into unemployment reduction. These results call for employment- and entrepreneurship-linked policy instruments that are timed to the lag structure of both effects and targeted at transforming necessity-driven activities into sustainable, high-value-added structures, rather than merely incentivizing firm entry. Aligning regional financial intermediation with employment creation can foster long-term socio-economic sustainability and promote sustainable regional development. Full article

Creativity relies on dynamic processing within the semantic network system; however, how this system varies under different cognitive strategies remains unclear. Grounded in the Associative Theory of Creativity, this study applied four cognitive strategies (application, analogy, abstraction, and combination) as distinct input constraints to the cognitive system. We tracked the N400 component using event-related potentials (ERPs), which capture brain activity time-locked to specific cognitive events. Specifically, the N400 serves as a reliable neural marker reflecting semantic mismatch and the integration of new information. Repeated-measures analysis of covariance (RM-ANCOVA) revealed that the abstraction strategy yielded the highest level of creativity, while the application strategy yielded the lowest. Neural data indicated that attenuated N400 amplitudes under the application strategy reflected minimal prediction errors within familiar conceptual spaces, whereas pronounced N400 amplitudes under abstraction and combination strategies represent substantial cognitive effort associated with feature extraction and concept integration. Subsequent linear mixed-effects model (LMM) analysis revealed that the N400 component exerted a significant negative moderating effect on individual creativity under the analogy strategy, establishing a boundary condition for transforming semantic updating into final creative output. By exploring associative processes through micro-neural mechanisms, this research provides practical insights for optimizing creative task design and evaluation structures. Full article

Recent developments in low-cost computing platforms have enabled new possibilities for real-time digital audio signal processing. In particular, the Raspberry Pi single-board computer provides an affordable system capable of performing audio processing tasks that previously required more capable hardware. We investigated the applicability of the Raspberry Pi for real-time granular synthesis, a technique that manipulates audio signals by modifying and rearranging short segments of the signal, known as grains. An accessible system for real-time granular synthesis is developed using the Raspberry Pi’s capabilities and efficient audio processing algorithms. The system’s performance is benchmarked on the basis of processing latency, audio output quality, and computational demands to determine the capabilities and feasibility of the platform for real-time granular audio applications. Results show that the Raspberry Pi is capable of achieving less than 20 ms latency for typical signals and sampling rates up to 48 kHz. The evaluation of audio output quality indicates minimal artifacts or noise compared to offline rendering. Measurements of CPU utilization demonstrate the effects of various grain parameters on computational load. These findings suggest promising opportunities to leverage affordable platforms like the Raspberry Pi for creative real-time granular synthesis projects across diverse fields. Full article

This paper investigates whether structured collaboration between multiple large language models (LLMs), each assigned a distinct cognitive role grounded in psychological theory, produces benefits beyond simple answer aggregation. We propose the Parallel Synthesis architecture, in which three cognitively specialized roles Analyzer (hierarchical decomposition), Creative (divergent thinking), and Critic (critical evaluation) process each task independently and in parallel, and a Synthesizer integrates their outputs into a final response. To evaluate collaborative reasoning, we introduce the Emergent Reasoning Score (ERS), a composite metric that separates perspective integration (Synthesis Effectiveness) from novel concept generation (Emergent Value). Experiments on Experiments on the AI2 Reasoning Challenge (ARC-Challenge) (1172 questions) and and the Massive Multitask Language Understanding benchmark (MMLU) (1531 questions) show two consistent findings. First, the architecture achieves high Synthesis Effectiveness ($SE=0.711$–$0.744$), indicating reliable integration of all three cognitive perspectives. Second, Emergent Value remains low ($EV=0.096$–$0.112$), indicating that synthesis primarily recombines existing concepts rather than generating substantial novel content. A Majority Voting baseline achieves comparable or slightly higher answer accuracy than the Synthesizer on both benchmarks, showing that the architecture’s main contribution lies not in answer selection but in producing integrated reasoning traces that draw on multiple perspectives. These findings suggest that the practical value of cognitively grounded multi-agent architectures lies in reliable perspective integration, while ERS provides a reusable framework for distinguishing integration from genuinely novel reasoning in multi-agent LLM systems. The empirical results reported here constitute a pilot validation of the proposed framework on closed-form benchmarks, intended to establish a proof of concept and motivate larger-scale evaluation. Full article

The rise of generative artificial intelligence (GenAI) is creating new opportunities for assessment design in universities, particularly in subjects that emphasize analytical and creative skills. This paper introduces the Educator–GenAI Partnership Model, an iterative five-stage model that helps educators create assessments that foster higher-order thinking (HOT). The model is grounded in constructive alignment and Bloom’s taxonomy, with a central emphasis on preserving human oversight to ensure educators retain control over assessment validity, academic integrity, and the ethical use of AI. The model maps out the unique strengths and responsibilities of both educators and GenAI, showing how each plays a distinct role in the assessment design process. It illustrates how GenAI can support the rapid generation of assessment tasks and marking rubrics, while positioning educators as critical decision-makers who only review, adapt, and iteratively refine AI-generated outputs to ensure alignment with higher-order learning outcomes. Overall, this paper presents a structured and practical model for utilizing GenAI responsibly in assessment design, thereby strengthening academic rigor while enhancing efficiency for educators. Full article

I recently introduced the concept of eco-cognitive openness and situatedness to explain how cognitive systems—human or artificial—dynamically interact with their environments to generate information and creative outputs through abductive cognition. Humans display high eco-cognitive openness, integrating tools and cultural contexts through “unlocked strategies” that also enable exceptional creativity. By contrast, generative AI like LLMs operates via “locked strategies” based on pre-existing datasets with limited real-time interaction, which constrains higher creativity. Although LLMs surpass humans in many cognitive tasks, they lack the openness required for truly advanced abductive performance. Notably, most human cognition is repetitive and imitative—humans themselves often resemble “stochastic parrots.” In this sense, LLMs reveal human intellectual poverty more than they expose flaws in artificial intelligence. I will illustrate how LLMs can act as powerful enhancers of human performance while simultaneously threatening our most distinctive prerogative: creativity. Future human–AI collaboration could expand our eco-cognitive openness, but demands vigilant oversight to counter bias and so-called overcomputationalization. GenAI can serve as an epistemic mediator toward unlocked creativity only if humans maintain agency and embed its outputs in broader socio-cultural frameworks. My greatest concern is that ethical and legal safeguards will remain ineffective in practice, resulting in mere “ethics washing” and “law washing” without genuine enforcement. Full article

The paper analyzes the structural transformations in tourism using the network input–output (IO) model. The study is based on IO tables for two years (2013 and 2023). This allows a comparative analysis of changes in the structure of technical coefficients and in multipliers associated with production and tax revenues. The approach enables the identification of changes in tourism’s position within the economic network. Tourism is also analyzed in terms of the degree of integration, dependence on intermediate inputs, and the capacity to spread the economic effects. The results show few upstream linkages for tourism. There is a low level of spillovers. To make it more resilient and generate more spillovers, it is important to build relationships with sectors such as agriculture, creative industries, and business services. The reliance on outsourced services could affect relationships with productive industries. Full article

Artificial Intelligence (AI) literacy and competency in pre-service teacher education refer to a programme-level implementation that enables teachers to work with AI systems effectively, critically, and ethically across university coursework, school placements, and early-career practice. This includes not only capability, but also professional enactment, where teachers apply AI-related knowledge in context-sensitive and pedagogically grounded ways. AI literacy refers to a shared knowledge base for understanding how AI systems generate outputs, how to evaluate and verify AI-supported information, and how to reason about task–tool fit in relation to fairness, privacy, transparency, accountability, academic integrity, equity, and environmental sustainability. AI competency refers to the application of this literacy in routine professional tasks, such as designing and justifying AI-informed teaching, learning, and assessment, protecting students’ and school data, documenting decisions, and revising AI-supported materials after checking for reliability, transparency, accountability, and equity. Together, literacy and competency extend beyond personal use of AI by preparing future teachers to support students’ creative, critical, and ethical engagement with AI, while keeping classroom practice aligned with educational goals, objectives, and values. Full article

Generative artificial intelligence tools are reshaping design by enabling novice designers to produce professional-quality user interfaces rapidly. However, for novice designers, exposure to AI-generated outputs that are far beyond their capabilities can inhibit creative growth. In this work, we investigate AI overperformance, when superior AI outputs lower the creative confidence of novices, and explore whether human-centred and explainable AI interfaces can mitigate such effects while sustaining creative agency. We conducted a within-subjects experiment with 75 novice designers using a web-based research platform. Participants completed mobile app design tasks under three conditions: Human-Only (baseline), AI Overmatch (exposure to superior AI outputs), and XAI-Enhanced (exposure to AI outputs with an embedded explainable interface). A repeated-measures ANOVA indicated that creative self-efficacy varied significantly, F = 24.67, p < 0.001, η² = 0.18. While creative self-efficacy was significantly decreased in the AI Overmatch condition, M = −1.18, SD = 0.32, when compared to the Human-Only conditions, M = 0.08, SD = 0.15, this was significantly increased in the XAI-Enhanced condition, M U= 0.42, SD = 0.18. This also led to a rise in creative performance across both ideation and output quality. The results showed that the AI Overmatch condition significantly reduced creative self-efficacy and originality; however, this negative effect was mitigated by the XAI-Enhanced interface, which enhanced confidence and idea quality. Mediation analysis demonstrated that expectancy disconfirmation explains the negative impact of AI overperformance on human creativity. These findings provide constructive design principles for educational AI tools and contribute to HCI theory by demonstrating that pedagogically oriented, transparent AI supports human–AI collaboration without diminishing human agency. Full article

Creativity involves generating ideas that are both original and useful, relying on intertwined cognitive and metacognitive processes. We examined how individual differences in semantic memory structure and ideation fluency predict creative performance and self-evaluations across two studies. In Study 1, participants completed a creative problem-solving (CPS) task, with semantic memory networks estimated from a relatedness judgment task. Creative output was assessed for originality and usefulness, alongside participants’ self-evaluations. In Study 2, a within-subjects design compared participants’ output and self-evaluation of their performance in a divergent thinking task (alternative uses task) and CPS. Results revealed that ideation fluency and semantic memory network integration consistently predicted originality across tasks. In contrast, usefulness was less reliably predicted, showing task-specific associations with semantic memory network properties primarily in CPS. Importantly, self-evaluations often diverged from objective outcomes, reflecting metacognitive biases shaped by heuristic cues. These findings highlight both stable and context-sensitive mechanisms in creative performance and self-evaluation. Full article