Search Results (130)

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

This study investigated whether humans and generative Large Language Models (LLMs) exhibit similar performance in divergent ideation but diverge in convergent selection. To address the critical oversight in current AI creativity research, which predominantly focuses on generative output, this study introduces the original conceptual framework of ‘Selection Alignment’ and a ‘novel dual-phase experimental protocol.’ This research transcends traditional generation-centric evaluations to establish a new paradigm for assessing the evaluative stage of creativity. A controlled experiment involved 240 design professionals (120 idea generators, 120 independent selectors) and two LLM agents (GPT-4o, Gemini 1.5 Pro). Participants and LLMs responded to identical divergent prompts, including 10 Alternative Uses Task-style prompts and 10 design problems. Both humans and LLMs generated candidate idea pools, then performed convergent selection by choosing the top five items per prompt. Idea generation was evaluated based on Fluency, Flexibility, and Semantic Breadth. Selection outcomes were compared using top-5 overlap rates derived from semantic clustering. The results indicated near-parity in generation metrics, showing no statistically significant differences between human and AI outputs. However, a substantial divergence was observed in convergent selection: the mean human–AI top-5 overlap was 19.2% for Model-A and 22.4% for Model-B, both significantly below permutation-based chance levels (null mean overlap ≈ 35%). AI selections were strongly predicted by embedding- and probability-based metrics, while human choices were better predicted by context- and experience-based criteria, highlighting a fundamental mechanistic divide. This suggests that convergent selection amplifies human–AI divergence, carrying significant implications for designing co-creative interfaces that integrate human experience into AI’s selection mechanisms. Full article

Current generative AI systems primarily utilize a prompt–response interaction model that restricts user intervention during the creative process. This lack of granular control creates a significant disconnect between user intent and machine output, which we define as the “Agency Gap”. This paper introduces the Agency-First Framework (AFF), which combines cognitive engineering and co-active design approaches to formally define human-AI collaboration. This is operationalized through the development of ten Generative AI Agency (GAIA) Heuristics, a systematic method for evaluating agency-centric interactions within stochastic generative settings. By translating the theoretical layers of the AFF into measurable criteria, the GAIA heuristics provide the necessary instrument for the empirical auditing of existing systems and the guidance of agency-centric redesigns. Unlike existing assistive AI guidelines that focus on output-level usability, the AFF establishes agency as a first-class design construct, enabling mid-process intervention and the steering of the model’s latent reasoning trajectory. Validation of the AFF was conducted through a two-tiered empirical evaluation: (1) an expert heuristic audit of state-of-the-art platforms, such as ChatGPT-o1 and Midjourney v6, which achieved high inter-rater reliability, and (2) a controlled redesign study. The latter demonstrated that agency-centric interfaces significantly enhance the Sense of Agency and Intent Alignment Accuracy compared to baseline prompt-response models, even when introducing a deliberate increase in task completion time—a phenomenon we describe as “productive friction” or an intentional interaction slowdown designed to prioritize cognitive engagement and user control over raw speed. Overall, the findings suggest that the restoration of meaningful user agency requires a shift from “seamless” system efficiency towards “productive friction”, where controllability and transparency within the generative process are prioritized. The major contribution of this work is the provision of a scalable, empirically validated framework and set of heuristics that equip designers to move beyond prompt-centric interaction, establishing a methodological foundation for agency-preserving generative AI systems. Full article

This study investigates how two widely used language-modelled generative AI tools, ChatGPT-5.1 (with DALL·E 3) and Gemini 3 (with Imagen), perform as early-stage co-design partners for biophilic interior design. Focusing on real-world use rather than theoretical capability, the research asks to what extent these systems can generate conceptually robust, visually coherent and practically feasible proposals when designers explicitly request biophilic strategies. A multiple-case design was employed across three scenarios: (1) an empty “tabula rasa” room, (2) a damaged rustic room requiring contextual renovation, and (3) a hospital staff break room to be transformed into a “cognitive restoration sanctuary.” For each case, both tools were prompted to produce a step-by-step biophilic design plan and a corresponding photorealistic image. Textual outputs were coded against the 14 Patterns of Biophilic Design and related restorative concepts, while images were evaluated by an expert panel of 15 architects with formal training in biophilic design using a structured Likert-scale instrument. Exterior and building-scale applications were not assessed. Results show that both systems can articulate broadly plausible biophilic strategies but differ in emphasis: ChatGPT tends to produce more spatially coherent, pattern-rich and functionally grounded plans, whereas Gemini excels more in visual realism and atmospheric rendering. Expert ratings indicate a consistent, though not overwhelming, preference for ChatGPT in spatial composition, human-spatial responses, contextual fit, and strategic support for cognitive restoration, with a slight advantage for Gemini in visual realism. Across all cases, however, plan-to-image fidelity is limited, particularly for non-visual and operational patterns (e.g., sound, scent, thermal variability, circadian systems, infrastructure access). The findings suggest that current generative AI tools are best positioned as fast, co-creative aides for early exploration of biophilic ideas, rather than as reliable autonomous consultants for evidence-based, cognitively targeted biophilic design. Full article

Through this qualitative systematic review, the authors ask the following: To what extent is the 300-word abstract fit for purpose in representing art and design practice-based research outputs on small and/or specialist institutional repositories? The abstract is an important part of the metadata when an Arts Practice-Based Output (APBO) is deposited on a repository. APBOs are non-traditional item types resulting from creative/artistic research processes. Examples include exhibitions, artefacts and digital videos. Little is known about how effectively these abstracts communicate research processes and insights across the art and design sector. This study aims to investigate how well the abstract communicates information about the arts practice-based research through a systematic review of APBOs. The eligibility criteria for inclusion in the review were as follows: APBOs must be from the date range January 2019 to January 2024, be an item type where the 300-word abstract is required, the abstract must be part of the publicly available metadata for the item, and outputs must be practice-based and from the art and design field. The date range (2019–2024) was employed because, during this time, APBOs had gained recognition in the wider research environment. APBOs from the reviewers’ institutional repository were not included in this study to avoid bias that could skew the results of the review. The data repositories from small and/or specialist Higher Education Institutions in the United Kingdom were searched for outputs which appeared to meet the eligibility criteria. These types of institution prioritise and produce more of these output types. A quality tool appropriate for creative/artistic research was applied to the identified dataset of APBOs. The resulting 27 APBOs’ 300-word abstracts were analysed using a thematic approach. Findings suggest that the 300-word abstracts contained information about the quality indicators such as whether the project got funding, the identities of prestigious collaborators and/or dissemination vehicles, and the international recognition of the research. Other identified themes were methodologies, contribution to knowledge, subject matter and item type. Full article

Large language models (LLMs) have shown strong potential for automated code generation in software development, yet their effectiveness in embedded systems programming—requiring understanding of software logic and hardware constraints—has not been well studied. Existing evaluation frameworks do not comprehensively cover practical microcontroller development scenarios in real-world Internet of Things (IoT) projects. This study systematically evaluates 27 state-of-the-art LLMs across eight embedded systems scenarios of increasing complexity, from basic sensor reading to complete cloud database integration with visualization dashboards. Using ESP32 microcontrollers with environmental and motion sensors, we employed the Analytic Hierarchy Process with four weighted criteria: functional, instructions, output and creativity, evaluated independently by two expert reviewers. Top-performing models were Claude Sonnet 4.5, Claude Opus 4.1, and Gemini 2.5 Pro, with scores from 0.984 to 0.910. Performance degraded with complexity: 19–23 models generated compilable code for simple applications, but only 3–5 produced functional solutions for complex scenarios involving Grafana and cloud databases. The most frequent failure was hallucinated non-existent libraries or incorrect API usage, with functional capability as the primary barrier and instruction-following quality the key differentiator among competent models. These findings provide empirical guidance for embedded developers on LLM selection and identify limitations of zero-shot prompting for hardware-dependent IoT development. Full article

Green finance is often viewed as being linked to sustainable growth, yet its effects may be uneven across regions with different industrial legacies. This paper examines how green finance correlates with green total factor productivity (GTFP) in China, with a focus on the country’s legacy industrial regions (broadly referred to as the “Rust Belt” in this paper), spanning Northeastern and Central China. Using a province–year panel for 30 mainland provinces over 2006–2023, we measure GTFP with a Slacks-Based Measure–Global Malmquist–Luenberger (SBM–GML) index that accounts for undesirable outputs. To reduce simultaneity concerns, we estimate two-way fixed-effects models and conduct robustness checks, including lag-based specifications; nevertheless, the observational design implies that the estimates should be interpreted as stable associations rather than definitive causal effects. We reveal a concerning stylized fact: despite rapid growth in green finance, GTFP in legacy industrial provinces exhibits a nonlinear pullback. More formally, we document pronounced regional heterogeneity: green finance is positively related to GTFP in eastern coastal provinces but negatively related to GTFP in central and northeastern legacy industrial provinces. Our findings are consistent with the theoretical prediction of an intertemporal mismatch in Schumpeterian creative destruction: standardized green-credit tightening coincides with tighter liquidity conditions for incumbent high-carbon sectors, while green entrants in these regions may scale up only gradually, leaving a temporary output and productivity “valley” during the transition. The results suggest that uniform green-finance policies may amplify transition risks in legacy industrial regions, motivating a shift from purely “green finance” toward complementary “transition finance” tools. Full article

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based on the Transformer framework was employed to perform sentiment quantification scoring for each comment; Sankey diagrams were used to quantitatively correlate spatial scenes with sentiment tendencies, thereby exploring the public’s perceptual associations with the architectural spatial environment of rural campuses. Full article

While creative Artificial Intelligence (AI) tools offer unprecedented creative power, their outputs often create a “personalization gap” by converging towards a generalized “average aesthetic” that ignores nuanced user preferences. This study addresses this challenge with a proof-of-concept computational framework to model and predict subjective color choices, aiming to make creative systems more human-centered. Our dual-track methodology attempts to decouple user preference into “inherent aesthetic profiles” and “contextual design decisions.” Through a dual-level study with 111 participants, we quantified inherent aesthetics into a vector library and trained a Gradient Boosting Decision Tree (GBDT) model on contextual data to predict design choices. The model achieved a predictive accuracy of 40.8%, and a grouped permutation importance analysis revealed the Product Category (Importance = 0.416) as the dominant predictor, providing evidence that design context is paramount. Crucially, a subsequent exploratory user validation study, analyzed with a linear mixed-effects model, showed our personalized recommendations were rated as significantly more satisfying (β = 1.278, p < 0.001) than those of a non-personalized baseline. This research provides a foundational framework for modeling subjective preference by distinguishing between stable traits and dynamic choices, offering a potential pathway to steer creative AI beyond generic outputs towards more personal and context-aware creative partners. Full article

This article introduces a theoretical framework centered on enhancing creativity through gaming, termed the Game-based Creativity Enhancement Framework (G-CEF). Rooted in experiential learning and game-based learning theories, the framework adopts an input–process–output paradigm: two inputs (personal attributes and game attributes), one process stage (learning situation), and outputs (learning improvements and acquisitions). Personal attributes take the form of conative dispositions and variables common to both creativity and games, which help explain why gaming habits and creativity are linked, particularly outside the laboratory. Six variables are identified and presented: playfulness, imagination, mind-wandering, mindfulness, psychological capital and motives. The second input corresponds to game attributes, which help explain why and how games can help improve creativity. Two forms of game attributes are presented: affordances and game mechanics. Eight types of affordances were identified: degree of flexibility, narrative, tools, environment, content creation, avatar, progression and replayability. Five types of game mechanics were also identified: originality, divergent thinking, convergent thinking, mental flexibility and creative dispositions. The learning situation within games represents a four-step cyclical experiential learning process: concrete experience, reflective observation, abstract conceptualization, and active experimentation. Lastly, the framework details enhancements in creativity due to gaming, supported by a literature review examining the impact of different game types on creativity. Full article

Problem: Entrepreneurship education continues to expand, yet it remains fragmented across disciplines and loosely connected to the knowledge, innovation, and venture ecosystems that shape entrepreneurial success. At the same time, AI is transforming research, collaboration, and venture development, but its use in education is typically limited to narrow, task-specific applications rather than ecosystem-level integration. Objective: This paper seeks to develop a comprehensive conceptual model for integrating AI into entrepreneurship education by positioning AI as a connective infrastructure that links and activates the knowledge, innovation, and venture ecosystems. Methods: The model is derived through an integrative synthesis of literature, programs, and activities on entrepreneurship education, ecosystem-based learning, and AI-enabled research and innovation practices, combined with an analysis of gaps in current educational approaches. Key Findings: The proposed model defines a progressive learning pathway consisting of (1) AI competency training that builds foundational capacities in critical judgment, responsible application, and creative adaptation; (2) AI praxis labs that use AI-curated ecosystem data to support iterative, project-based learning; and (3) venture studios where students scale outputs into innovations and ventures through structured ecosystem engagement. This pathway demonstrates how AI can function as a structural mediator of problem definition, research design, experimentation, analysis, and narrative translation. Contributions: This paper reframes entrepreneurship education as an iterative, inclusive, and ecosystem-connected process enabled by AI infrastructure. It offers a new theoretical lens for understanding AI’s educational role and provides actionable implications for curriculum design, institutional readiness, and policy development while identifying avenues for future research on competency development and ecosystem impacts. Full article

This study explores an alternative design methodology for architectural image generation using generative AI, addressing the challenge of how AI-generated imagery can preserve formative tendencies while enabling creative variation and user agency. Departing from conventional prompt-based approaches, the process utilizes only a minimal initial image set and proceeds by reintroducing solely the synthesized outcomes during the blending and iterative synthesis stages. The central research question asks whether AI can sustain and transform architectural tendencies through iterative synthesis despite limited input data, and how such tendencies might accumulate into consistent typological patterns. The research examines how formative tendencies are preserved and transformed, based on four aesthetic elements: layer, scale, density, and assembly. These four elements reflect diverse architectural ideas in spatial, proportional, volumetric, and tectonic characteristics commonly observed in architectural representations. Observing how these tendencies evolve across iterations allows the study to evaluate how AI negotiates between structural preservation and creative deviation, revealing the generative patterns underlying emerging AI typologies. The study employs SSIM, LPIPS, and CLIP similarity metrics as supplementary indicators to contextualize these tendencies. The results demonstrate that iterative blending enables the deconstruction and recomposition of archetypal formal languages, generating new visual variations while preserving identifiable structural and semantic tendencies. These outputs do not converge into generalized imagery but instead retain identifiable tendencies. Furthermore, the study positions user selection and intervention as a crucial mechanism for mediating between accidental transformation and intentional direction, proposing AI not as a passive generator but as a dialogical tool. Finally, the study conceptualizes such consistent formal languages as “AI Typologies” and presents the potential for a systematic design methodology founded upon them as a complementary alternative to prompt-based workflows. Full article

Generative artificial intelligence (GenAI) tools are transforming art education by enabling instant creation of textual, visual, audio, and multimodal outputs. This systematic review synthesizes research on GenAI applications in art education from January 2019 to August 2025. Following PRISMA 2020 guidelines, 19 peer-reviewed empirical studies across six databases (Web of Science, ScienceDirect, Springer, Taylor & Francis, Scopus, and ERIC) met inclusion criteria, which required clear pedagogical implementation with students or educators as active participants. Research accelerated from two studies in 2023 to 14 in 2025, with most studies examining higher education and East Asia contexts through mixed methods approaches and grounded in constructivist and cognitive learning theories. Text-to-image generation models (DALL-E, Midjourney, Stable Diffusion) and conversational AI (ChatGPT) were most frequently implemented across creative production, pedagogical scaffolding, and instructional design applications. Findings from this emerging body of research suggest that GenAI has the potential to improve learning achievement, creative thinking, engagement, and cultural understanding when integrated through structured pedagogical frameworks with intentional instructor design. However, these positive outcomes represent early-stage implementation trends in well-resourced contexts rather than broadly generalizable conclusions. Successful integration requires explicit instructional frameworks, clear ethical guidelines for human-AI collaboration, and evolved assessment methods. Full article

This paper introduces a novel framework for sensory representation of brain imaging data, combining deep learning-based segmentation with multimodal visual and auditory outputs. Structural magnetic resonance imaging (MRI) predictions are converted into color-coded maps and stereophonic/MIDI sonifications, enabling intuitive interpretation of cortical activation patterns. High-precision U-Net models efficiently generate these outputs, supporting clinical decision-making, cognitive research, and creative applications. Spatial, intensity, and anomalous features are encoded into perceivable visual and auditory cues, facilitating early detection and introducing the concept of “auditory biomarkers” for potential pathological identification. Despite current limitations, including dataset size, absence of clinical validation, and heuristic-based sonification, the pipeline demonstrates technical feasibility and robustness. Future work will focus on clinical user studies, the application of functional MRI (fMRI) time-series for dynamic sonification, and the integration of real-time emotional feedback in cinematic contexts. This multisensory approach offers a promising avenue for enhancing the interpretability of complex neuroimaging data across medical, research, and artistic domains. Full article